Probable Relationship Research Articles

PSATF-6mA: an integrated learning fusion feature-encoded DNA-6 mA methylcytosine modification site recognition model based on attentional mechanisms

DNA methylation is of crucial importance for biological genetic expression, such as biological cell differentiation and cellular tumours. The identification of DNA-6mA sites using traditional biological experimental methods requires more cumbersome steps and a large amount of time. The advent of neural network technology has facilitated the identification of 6 mA sites on cross-species DNA with enhanced efficacy. Nevertheless, the majority of contemporary neural network models for identifying 6 mA sites prioritize the design of the identification model, with comparatively limited research conducted on the statistically significant DNA sequence itself. Consequently, this paper will focus on the statistical strategy of DNA double-stranded features, utilising the multi-head self-attention mechanism in neural networks applied to DNA position probabilistic relationships. Furthermore, a new recognition model, PSATF-6 mA, will be constructed by continually adjusting the attentional tendency of feature fusion through an integrated learning framework. The experimental results, obtained through cross-validation with cross-species data, demonstrate that the PSATF-6 mA model outperforms the baseline model. The in-Matthews correlation coefficient (MCC) for the cross-species dataset of rice and m. musus genomes can reach a score of 0.982. The present model is expected to assist biologists in more accurately identifying 6 mA locus and in formulating new testable biological hypotheses.

Evaluating the causal effects between Grave’s disease and diabetes mellitus: a bidirectional Mendelian randomization study

BackgroundGraves’ disease (GD) is an autoimmune disease associated with an increased incidence of other autoimmune diseases. To investigate the causality between GD and Diabetes mellitus (DM), we designed bidirectional two-sample Mendelian randomization (MR) and multivariable MR (MVMR) studies.MethodsSingle-nucleotide polymorphisms (SNPs) associated with GD, thyroid peroxidase (TPO), thyroglobulin (Tg), thyroid-stimulating hormone (TSH), type 1 diabetes (T1D), and type 2 diabetes (T2D) were obtained from the IEU Open GWAS and FinnGen biobank databases. For the forward MR study, we used GD (sample size = 458,620) as the exposure and T1D (sample size = 520,580) and T2D (sample size = 211,766) as the outcomes. Next, high risk of T1D and T2D were used as exposure variables, and GD was used as the outcome variable for the reverse MR analysis. Finally, MVMR analysis was conducted to investigate the probable relationship between DM and indicators for thyroid function like TPO, Tg, and TSH. The inverse variance weighting (IVW) was used as the main method. Finally, the heterogeneity and sensitivity were assessed.ResultsThere were 27, 88, and 55 SNPs associated with GD, T1D, and T2D, respectively. A significant causal connection between higher genetic liability of GD and the risk of T2D (OR [95% CI] = 1.059 [1.025–1.095], P = 5.53e-04) was found in the forward MR analysis. Comparatively, the significant causal relationship between higher genetic liability of GD and the risk of T1D was not demonstrated (OR [95% CI] = 0.998[0.927,1.074], P=0.949). However, reverse MR suggested that there was a genetic susceptibility to T1D that increased the likelihood of developing GD (OR [95% CI] = 1.173[1.117,1.231], P = 1.913e-10), while T2D did not (OR [95% CI] = 0.963 [0.870–1.066], P = 0.468). Furthermore, there was inadequate evidence to suggest that abnormal TSH, TPO, and Tg levels increase the risk of incident T1D or T2D in individuals with GD. MVMR revealed no causal relationship among Tg, TSH, TPO, T1D, or T2D.ConclusionThere was no increased risk of T1D with an increase in genetic susceptibility to GD, although higher genetic susceptibility to T1D has been shown to be associated with increased risk of developing GD. A unidirectional causal relationship between the genetic liability for GD and increased risk of T2D was observed using MR analyses. MVMR analysis showed no statistically relevant causality between the genetic liability for TSH, TPO, or Tg and the risk of either T1D or T2D.

High frequency radar error classification and prediction based on K-means methods

This study aims to characterize the high frequency radar and numerically simulated low-frequency filtered currents in the south-eastern Bay of Biscay (study area) using a K-means classification algorithm based on an improved Euclidean Distance calculation method that does not take missing values. The errors between observations and simulations was estimated and predicted based on this classification method. Results indicate that predominantly eastward (northward) currents over the Spanish (French) continental shelf/slope in winter and more variable currents in the west and south-west in summer. The model classification results for circulation characteristics are in relatively good agreement with HF radar results, especially for currents on the Spanish (French) shelf/slope. In addition, the probabilistic relationship between observed and modeled currents was explored, obtaining the probability of occurrence of modeled current groups when each group of observed currents occurs. Finally, predictions of model and observed current errors were made based on the classification results, and it was found that the predictions based on the classification of all data had the smallest errors, with a 17% improvement over the unclassified control experiment. This study provides a foundation for subsequent model error testing, forecast product improvement and data assimilation.

Interpretable logical-probabilistic approximation of neural networks

The paper proposes the approximation of DNNs by replacing each neuron by the corresponding logical-probabilistic neuron. Logical-probabilistic neurons learn their behavior based on the responses of initial neurons on incoming signals and discover all logical-probabilistic causal relationships between the input and output. These logical-probabilistic causal relationships are, in a certain sense, most precise – it was proved in the previous works that they are theoretically (when probability is known) can predict without contradictions. The resulting logical-probabilistic neurons are interconnected by the same connections as the initial neurons after replacing their signals on true/false. The resulting logical-probabilistic neural network produces its own predictions that approximate the predictions of the original DNN. Thus, we obtain an interpretable approximation of DNN, which also allows tracing of DNN by tracing its excitations through the causal relationships. This approximation of DNN is a Distillation method such as Model Translation, which train alternative smaller interpretable models that mimics the total input/output behavior of DNN. It is also locally interpretable and explains every particular prediction. It explains the sequences of logical probabilistic causal relationships that infer that prediction and also show all features that took part in this prediction with the statistical estimation of their significance. Experimental results on approximation accuracy of all intermedia neurons, output neurons and softmax output of DNN are presented, as well as the accuracy of obtained logical-probabilistic neural network. From the practical point of view, interpretable transformation of neural networks is very important for the hybrid artificial intelligent systems, where neural networks are integrated with the symbolic methods of AI. As a practical application we consider smart city.

The time lag in local field potential signals for the development of its Bayesian belief network

PurposeThe objective is to suggest time as an important variable to consider in the network model, specifically when discussing causality.MethodsThere is a consideration of the context of functional connectivity because of the time importance of observing the feature inside the neuroscience context. A network model was constructed using the Bayesian network method, utilizing a dataset consisting of three rats’ local field potentials. The model took into consideration the time delay of communication among brain areas, as recorded in this study. In pursuit of this objective, the delayed mutual information method was employed to ascertain the temporal delay between local field potentials and K2 score for the purpose of model comparison.ResultsBayesian network depicted the probabilistic relationship among rat’s brain areas. Delayed mutual information captured the lag among brain areas, and after its appliance on the Bayesian network model, posed better results.ConclusionThe primary novelty of this research lies in its integration of minor delays within the Bayesian network approach, accomplished through the utilization of the delayed mutual information technique prior to its implementation. The suggested methodology incorporates an essential feature that supports the analysis of functional connectivity among brain areas, thereby providing support for the dynamics of neurophysiology.

Floating photovoltaic systems: photovoltaic cable submersion testing and potential impacts.

Floating photovoltaics (FPV) is an emerging technology that is gaining attention worldwide. However, little information is still available on its possible impacts in the aquatic ecosystems, as well as on the durability of its components. Therefore, this work intends to provide a contribution to this field, analysing possible obstacles that can compromise the performance of this technology, adding to an increase of its reliability and assessing possible impacts.The problem under study is related to the potential submersion of photovoltaic cables, that can lead to a degradation of its electrical insulation capabilities and, consequently, higher energy production losses and water contamination. In the present study, the submersion of photovoltaic cables (with two different insulation materials) in freshwater and artificial seawater was tested, in order to replicate real life conditions, when FPV systems are located in reservoirs or in the marine environment. Electrical insulation tests were carried out weekly to assess possible cable degradation, the physical-chemical characteristics of the water were also periodically monitored, complemented by analysis to detect traces of copper and microplastics in the water. The results showed that the submersion of photovoltaic cables with rubber sheath in saltwater can lead to a cable accelerated degradation, with reduction of its electrical insulation and, consequently, copper release into the aquatic environment. The test results pointed a probable relationship between submersion of cables with rubber outer shell and water freezing temperatures and the occurrence of accelerated degradation of the cable insulation layer. Reduced insulation resistance values were measured in this cable type after the occurrence of such temperatures, both in salt and freshwater, the cable presented visible exterior degradation signs. For this case copper residues were detected in the water.

Evaluation of the natural killer cell subsets and their relationship with serum interferon gamma and vitamin D levels in women with stages III and IV endometriosis: A case-control study.

Natural killer (NK) cells play a critical role in the pathogenesis of endometriosis. Moreover, a normal vitamin D level is remarkably associated with an optimal immune response. So, there may be a probable relationship between these factors and the endometriotic women. This study aimed to evaluate the percentage of NK cells and their subsets and their relationship with serum levels of vitamin D and interferon-gamma (IFN-γ) in women with endometriosis. In this case-control study, 29 women with stage III-IV endometriosis and 30 healthy controls were enrolled. The study was conducted in the Immunology Department of Isfahan University of Medical Sciences, Isfahan, Iran between November 2021 and June 2022. The percentage of NK cells and their subsets, including CD56 CD16 , CD56 CD16 and CD56 CD16 were measured in the peripheral blood samples using flow cytometry. Serum levels of vitamin D and IFN-γ were also measured using the enzyme-linked immunosorbent assay. The mean percentage of NK cells in women with endometriosis increased significantly compared to the control group (p = 0.03). The percentage of CD56 CD16 (p = 0.007) and CD56 CD16 (p = 0.043) increased significantly in women with endometriosis in comparison with the control group, but the percentage of CD56 CD16 subset was not significantly different. No relationship was observed between NK cells and their subsets with vitamin D and IFN-γ in the studied groups. The study of NK cell subsets and their related factors can be useful in assessing and treating women suffering from endometriosis. However, more comprehensive studies are required to draw definitive conclusions about these observations.

An In-depth Analysis of the Adverse Effects of Ionizing Radiation Exposure on Cardiac Catheterization Staffs.

Diagnostic and interventional angiograms are instrumental in the multidisciplinary approach to CAD management, enabling accurate diagnosis and effective targeted treatments that significantly enhance patient care and cardiovascular outcomes. However, cath lab staff, including interventional cardiologists, is consistently exposed to ionizing radiation, which poses inherent health risks. Radiation exposure in the cath lab primarily results from the use of fluoroscopy and cineangiography during diagnostic and interventional procedures. Understanding these risks and implementing effective radiation protection measurements are imperative to ensure the well-being of healthcare professionals while delivering high-quality cardiac care. Prolonged and repeated exposure can lead to both deterministic and stochastic effects. Deterministic effects, such as skin erythema and tissue damage, are more likely to occur at high radiation doses. Interventional cardiologists and staff may experience these effects when safety measures are not rigorously followed. In fact, while ionizing radiation is essential in the practice of radiation cardiology ward, cath lab staff faces inherent risks from radiation exposure. Stochastic effects, on the other hand, are characterized by a probabilistic relationship between radiation exposure and the likelihood of harm. These effects include the increased risk of cancer, particularly for those with long-term exposure. Interventional cardiologists, due to their frequent presence in the cath lab, face a higher lifetime cumulative radiation dose, potentially elevating their cancer risk. Protective measures, including the use of lead aprons, thyroid shields, and radiation monitoring devices, play a crucial role in reducing radiation exposure for cath lab personnel. Adherence to strict dose optimization protocols, such as minimizing fluoroscopy time and maximizing distance from the radiation source, is also essential in mitigating these risks. Ongoing research and advancements in radiation safety technology are essential in further for minimizing the adverse effects of ionizing radiation in the cath lab.

A Bayesian network analysis of the probabilistic relationships between metabolically healthy obesity and cardiovascular disease risk under new diagnostic criteria.

The relationship between metabolically healthy obesity (MHO) and cardiovascular disease (CVD) risk remains debated. The critical point may be the lack of consensus on MHO's definition and diagnostic criteria. This study aimed to investigate the association of MHO status with arteriosclerosis-CVD (ASCVD) risk in Chinese under new diagnostic criteria. Participants aged 35-79 in the 2009 China Health and Nutrition Survey cohort were included. The 10-year ASCVD risk was predicted by the prediction for ASCVD risk in China, and participants with a predicted risk of ⩾ 10% were classified into the high-risk group. The Bayesian network (BN) models were constructed to characterize the multivariable probabilistic connections between metabolically obesity phenotypes and ASCVD risk. The 10-year ASCVD risk score and the proportion of individuals at ASCVD high risk were significantly different between metabolically obesity phenotypes (P< 0.001). BN reasoning results showed that MHO individuals were not significantly associated with a 10-year ASCVD risk. Among metabolically unhealthy individuals, the conditional probability of high ASCVD risk increased with the Body Mass Index (BMI), with the conditional probability of high ASCVD risk was 24.63% (95% CI: 22.81-26.55%), 32.97% (95% CI: 30.75-35.27%) and 40.2% (95% CI: 36.64-43.86%) for metabolically unhealthy normal weight (MUNW), metabolically healthy overweight weight (MHOW), and metabolically unhealthy obesity (MUO) group, respectively. Subgroup analysis showed that MHO individuals were at increased risk of CVD compared with metabolically healthy normal weight (MHNW) individuals only in females. These results showed that there was no significant increase in ASCVD risk of MHO phenotype based on the new diagnostic criteria, suggesting that MHO is in a relatively healthy state.

Is the Interfacial Electrochemical Behavior of Quercetin the Same as That of Catechol Plus Resorcinol?

Background: Electrodeposition of functional films from polyphenol-containing solutions has emerged as a new field of surface functionalization from bio-sourced molecules. There is, however, almost no knowledge about the chemical structure of such complex films. It is the aim of this research to use the known electrodeposition of films made from catechol and resorcinol, two isomers of dihydroxybenzene, to understand the electrodeposition of a more complex polyphenol, quercetin, which is constituted from a fused catechol and resorcinol moiety. The aim of this article is hence to introduce some methodology in the interpretation of the electrochemical behavior of complex polyphenols starting from their building blocks. Methods: Cyclic voltammetry (CV) is used to deposit films from quercetin and from equimolar blends of catechol and resorcinol on amorphous carbon and gold working electrodes. The main experimental parameter was the potential sweep rate used during the CVs. Results: The CV of quercetin is not the exact sum of the CV of the catechol + resorcinol blends, but the major features are conserved, namely the presence of two main oxidation peaks affiliated to those of catechol and resorcinol but shifted to less anodic potentials. In addition, the anodic electron transfer coefficients of the two oxidation waves of quercetin are higher than those measured in the catechol resorcinol blend. However, film deposition ability is reduced with quercetin compared to catechol + resorcinol blend in probable relationship to steric hindrance occurring during the non-electrochemical crosslinking of the deposit. The quercetin-based films deposited at 10 mV·s−1 on gold electrodes are conformal and display some antioxidant activity.

DNA N-gram analysis framework (DNAnamer): A generalized N-gram frequency analysis framework for the supervised classification of DNA sequences

In 1948, Claude Shannon published a mathematical system describing the probabilistic relationships between the letters of a natural language and their subsequent order or syntax structure. By counting unique, reoccurring sequences of letters called N-grams, this language model was used to generate recognizable English sentences from N-gram frequency probability tables. More recently, N-gram analysis methodologies have been successfully applied to address many complex problems in a variety of domains, from language processing to genomics. One such example is the common use of N-gram frequency patterns and supervised classification models to determine authorship and plagiarism. In this paradigm, DNA is a language model where nucleotides are analogous to the letters of a word and nucleotide N-grams are analogous to the words of a sentence. Because DNA contains highly conserved and identifiable nucleotide sequence frequency patterns, this approach can be applied to a variety of classification and data reduction problems, such as identifying species based on unknown DNA segments. Other useful applications of this methodology include the identification of functional gene elements, microorganisms, sequence contamination, and sequencing artifacts. To this end, I present DNAnamer, a generalized and extensible methodological framework and analysis toolkit for the supervised classification of DNA sequences based on their N-gram frequency patterns.

Age-dependent association between aneurysmal and isolated redundant inter-atrial septum with patent foramen ovale.

Interatrial septum (IAS) could be redundant by abnormal movement with or without aneurysmal bulging. We aimed to determine the prevalence of isolated redundant and aneurysmal IAS motion and their probable relationship with age and presence of patent foramen ovale (PFO) in a large-scale referral center. A total of 15,288 patients who underwent transthoracic echocardiography (TTE) at Tehran Heart Center were included From March 2019 to March 2021. Contrast echocardiography was conducted on patients with suspected PFO. Data were retrospectively analyzed from our previously established database. The prevalence of aneurysmal and redundant IAS and their relationship with age and PFO presence was evaluated. Aneurysmal and isolated redundant IAS were present in 4% and 12.4% of the participants, respectively. The aneurysmal group was older, more frequently female and had higher frequency of PFO. The frequency of PFO among patients with aneurysmal IAS was about 14.4% and 5.3% in isolated redundant IAS patients. In both groups, the prevalence of abnormal IAS motion increased across age groups from the youngest to the oldest, while the prevalence of PFO decreased. In conclusion, the frequency of PFO among patients with aneurysmal IAS was about 2.5 times more than that among patients with isolated redundant IAS and 24 times more than the normal population. The increasing trend observed across the aging group proposes a potential role for age in the pathophysiology of abnormal IAS. Furthermore, the decreasing prevalence of PFO among patients underlies its clinical significance as an important risk factor.

Self-supervised probabilistic models for exploring shape memory alloys

Recent advancements in machine learning (ML) have revolutionized the field of high-performance materials design. However, developing robust ML models to decipher intricate structure-property relationships in materials remains challenging, primarily due to the limited availability of labeled datasets with well-characterized crystal structures. This is particularly pronounced in materials where functional properties are closely intertwined with their crystallographic symmetry. We introduce a self-supervised probabilistic model (SSPM) that autonomously learns unbiased atomic representations and the likelihood of compounds with given crystal structures, utilizing solely the existing crystal structure data from materials databases. SSPM significantly enhances the performance of downstream ML models by efficient atomic representations and accurately captures the probabilistic relationships between composition and crystal structure. We showcase SSPM’s capability by discovering shape memory alloys (SMAs). Amongst the top 50 predictions, 23 have been confirmed as SMAs either experimentally or theoretically, and a previously unknown SMA candidate, MgAu, has been identified.

Interpretation of results of PCR and B-D-glucan for the diagnosis of Pneumocystis Jirovecii Pneumonia in immunocompromised adults with acute respiratory failure

BackgroundThe accuracy of a diagnostic test depends on its intrinsic characteristics and the disease incidence. This study aims to depict post-test probability of Pneumocystis pneumonia (PJP), according to results of PCR and Beta-D-Glucan (BDG) tests in patients with acute respiratory failure (ARF).Materials and methodsDiagnostic performance of PCR and BDG was extracted from literature. Incidence of Pneumocystis pneumonia was assessed in a dataset of 2243 non-HIV immunocompromised patients with ARF. Incidence of Pneumocystis pneumonia was simulated assuming a normal distribution in 5000 random incidence samples. Post-test probability was assessed using Bayes theorem.ResultsIncidence of PJP in non-HIV ARF patients was 4.1% (95%CI 3.3-5). Supervised classification identified 4 subgroups of interest with incidence ranging from 2.0% (No ground glass opacities; 95%CI 1.4–2.8) to 20.2% (hematopoietic cell transplantation, ground glass opacities and no PJP prophylaxis; 95%CI 14.1–27.7). In the overall population, positive post-test probability was 32.9% (95%CI 31.1–34.8) and 22.8% (95%CI 21.5–24.3) for PCR and BDG, respectively. Negative post-test probability of being infected was 0.10% (95%CI 0.09–0.11) and 0.23% (95%CI 0.21–0.25) for PCR and BDG, respectively. In the highest risk subgroup, positive predictive value was 74.5% (95%CI 72.0-76.7) and 63.8% (95%CI 60.8–65.8) for PCR and BDG, respectively.ConclusionAlthough both tests yield a high intrinsic performance, the low incidence of PJP in this cohort resulted in a low positive post-test probability. We propose a method to illustrate pre and post-test probability relationship that may improve clinician perception of diagnostic test performance according to disease incidence in predefined clinical settings.

Accounting for ground-motion uncertainty in empirical seismic fragility modeling

Seismic fragility models provide a probabilistic relation between ground-motion intensity and damage, making them a crucial component of many regional risk assessments. Estimating such models from damage data gathered after past earthquakes is challenging because of uncertainty in the ground-motion intensity the structures were subjected to. Here, we develop a Bayesian estimation procedure that performs joint inference over ground-motion intensity and fragility model parameters. When applied to simulated damage data, the proposed method can recover the data-generating fragility functions, while the traditionally used method, employing fixed, best-estimate, intensity values, fails to do so. Analyses using synthetic data with known properties show that the traditional method results in flatter fragility functions that overestimate damage probabilities for low-intensity values and underestimate probabilities for large values. Similar trends are observed when comparing both methods on real damage data. The results suggest that neglecting ground-motion uncertainty manifests in apparent dispersion in the estimated fragility functions. This undesirable feature can be mitigated through the proposed Bayesian procedure.

Bidirectional Association between Periodontitis and Thyroid Disease: A Scoping Review.

Periodontitis is a chronic inflammatory disease of the tissues surrounding and supporting the teeth. Due to the development of chronic inflammation, periodontitis can contribute to the development of several systemic diseases, including thyroid disease. Thyroid pathology includes benign, malignant, and autoimmune conditions leading to hypothyroidism, hyperthyroidism, or euthyroidism. Alterations in thyroid hormones, especially hypothyroidism, can reveal significant oral manifestations, including periodontitis. This scoping review aims to explore the probable causal relationship between periodontitis and thyroid disease, in terms of epidemiology, pathogenesis, and treatment. The search strategy follows the PRISMA-ScR guidelines. PubMed, Scopus, Web of Science, and Cochrane were searched from January 2014 to January 2024, entering the MESH terms "periodontitis" and "thyroid". Of 153 initial records, 20 articles were selected and discussed. There is a high prevalence of periodontitis among patients with thyroid disease, including thyroid cancer. The causes at the basis of this association are genetic factors, the oral microbiome, and proinflammatory cytokines. Periodontal treatment, specifically scaling and root planning, can ameliorate thyroid parameters. Although there are a few randomized controlled studies in the literature, this review lays the foundation for a bidirectional relationship between periodontitis and thyroid disease, the link to which is, once again, systemic inflammation.

Salivary Biophysical Properties in Relation to Dental Caries Severity Among Type 1 Diabetic Children

Diabetes Mellitus is a multifactorial metabolic disorder characterized by elevated blood glucose levels or hyperglycemia. Diabetes Mellitus may cause many oral problems as a result of both micro- and macrovascular complications. We aimed to assess the probable relationship between the severity of dental caries and the biophysical properties of saliva (flow rate, PH, and viscosity). Materials and Methods: A cross-sectional comparative study was conducted in Karbala City, in which a sample of 45 children with type 1 diabetes aged between 10-12 years was compared with a group of 45 non-diabetic children matched in age and gender. Dental caries severity and experience were recorded and diagnosed using Manji et al.'s criteria and the decayed, missing, and filling index (DMFS/dmfs). Unstimulated salivary specimens were collected to evaluate flow rate, pH, and viscosity. Pearson (r) correlation coefficient test and one-way ANOVA test were employed for data analysis using SPSS-22. Results: Salivary levels of viscosity and flow rate were not statistically significant (P &gt; 0.05) predicators regarding the dental caries experience and severity among type1 diabetic children (controlled and uncontrolled) while pH level of saliva was a statistically significant predictor regarding dental caries experience (DS/ DMFT) among uncontrolled diabetic children. Conclusion: Compared to non-diabetic children, type 1 diabetic children exhibited pronounced changes in salivary biophysical properties but unpronounced changes in caries status.

Microplastics in urban water systems, Tehran Metropolitan, Iran.

Urban water systems are potential sources of secondary microplastics (MPs) as well as a distributor of MPs in the environment. In the present study, the presence of MPs in the urban water systems of the Tehran Metropolitan (Capital of Iran) was investigated. In addition, the probable relationship of MPs with different land uses (i.e., residential-commercial, forest, military, and highway) was assessed. The results showed that all parts of Tehran's urban water system in the study area were contaminated with MPs (107.1 ± 39, 37.8 ± 10.5, 48.3 ± 3.1, 46.9 ± 5.6, 59.4 ± 26.5, 1.7, 2.0 ± 0.6, 7.9 ± 1, 1.8 ± 0.2 particles/liter at the residential, integrated, military, forest, highway runoffs, drinking water, groundwater, seasonal river, and the effluent of the wastewater treatment plants; respectively). However, significant differences were found between different land uses. As expected, the residential runoff had the highest rate of MPs pollution, with 107.1 ± 39 particles/liter. According to the obtained results and our estimation, more than five million MPs/day can enter into the water bodies and soil of the study area through the wastewater treatment plants. While there are significant differences in MPs in the different land uses, our findings suggest that residential areas and highways need further attention in controlling the spread of MPs in urban areas.

Aspirations of agriculture school students

The present investigation on “Aspirations of agriculture school students” was conducted in Amravati district, where Achalpur, Amravati and Warud Talukas were selected. Under these three talukas, three colleges viz. Agricultural Polytechnic School, Bhilona, Taluka:Achalpur; Janata Agricultural Polytechnic School, Amravati, Taluka: Amravati and Late Punjabrao Thakre Agricultural Polytechnic School, Haturna, Taluka: Warud were selected under the jurisdiction of Dr. Panjabrao Deshmukh Krishi Vidhyapeeth, Akola. These three colleges are under Amravati district. Social, situational, economic and psychological environment in these three schools differs significantly because of their location therefore study was conducted with the view to know the aspirations of agriculture school students.The study revealed that the majority of 75.00 per cent were male and remaining i.e. 25.00 per cent were female. Hence, majority of students were under male category. It was observed that the majority of the respondents 43.34 per cent have semi-medium land holding, followed by 33.33 per cent of the respondents families had medium land holding. The majority 69.17 per cent of respondents had medium level of father’s education whereas the majority 55.00 per cent of respondents had medium level of mother’s education’. It was observed that the majority of respondents i.e. 60.84 per cent belonged to nuclear family. The majority of respondent’s parents i.e. 66.67 per cent had only agriculture as their occupation. The majority of respondents i.e. 68.33 per cent had secured ‘Distinction’ class in academic performance. The majority of respondents i.e. 44.16 per cent had high level of family income. The majority of agriculture school students i.e. 64.16 per cent had medium level of social participation. The majority of respondents i.e.73.33 per cent had medium level of mass media utilization.In case of overall aspirations, majority i.e. 72.5 per cent of the respondents have medium level of overall aspirations while 15.84 per cent have high level of overall aspirations while 11.66 per cent of the respondents were having low level of overall aspirations.The independent variables like land holding, mother education, parental occupation, and academic performance showed positive and highly significant (at 0.01 % level of probability) relationship with overall aspirations of agriculture school students. Whereas father education, annual family income, social participation and mass media utilization show a positive and significant (at 0.05 % level of probability) relationship with overall aspirations of agriculture school students. However, gender and family type was not significant with overall aspirations of agriculture school students.

Confidence based compressed sensing approach with QoS differentiation for reliable grant-free access

With the rapid development of 5G technology, numerous intelligent terminals can be successively connected to a network. This brings significant challenges to existing network access because it increases the collision probability. It is essential for Grant-free random access (GF-RA) to ensure low delay and reliable transmission. Due to multiuser interference and varying environments, user detection can be complicated, which leads to unreliable transmission. To address the limitations above, research on the GF-RA method based on compressed sensing is carried out and it includes the following: (1) The confidence-weighted orthogonal matching pursuit method is used for estimating the number of active users and overcomes the constraint of sparse condition. (2) The confidence-weighted simultaneous hard thresholding (SHT) method is used for active user detection (AUD), which can improve the performance of the SHT algorithm. (3) We conduct collision probability analysis to not only provide the performance upper bound using the multiple preambles method but also infer the collision probability relationship with different priorities. This is helpful to realize diverse quality of service requirements. The numerical results prove that the proposed method is valid for non sparse signals and can realize a high success access rate.