Cheating is a kind of fraud or attempts to get dishonest advantages in an academic institution, it has been increasing dramatically for the last 50 years with the growth of technology. In some countries, this phenomenon has changed to a problematic factor in schools. Plagiarism is also considered as a type of cheating if it occurs intentionally. More efforts are made to mitigate the severity of cheating in an academic institution using technological-based devices. Besides, technology has an effective role in the detection of plagiarism by several detection platforms, however, still, there are some types of plagiarism which cannot be detected by these platforms efficiently, that authors should treat honestly during his/her scientific writing. This work aims to conceptualize and characterize cheating and plagiarism in an academic institution, also it is tried to indicate the relationship of cheating and plagiarism that in which cases, plagiarism becomes cheating. To achieve the purposes, relevant literature including scientific papers, monographs, reports, and authorized website were reviewed, and the literature output are integrated with the observation and experience of author during the teaching and learning in academic institutions.The findings and direct observation from close contact of classrooms show that cheating is felt mostly in classes with large numbers of students, that institution policy, instructor control, and using of technology have the most effect on the controlling cheating. Plagiarism as academic dishonesty is a widespread phenomenon among the students and even the authors. In some cases, the poor understanding of students and authors in scientific writing rules causes plagiarism and is measurable by some available detection platforms.

Geospatial Information Systems (GIS) can provide a great environment for using machine learning algorithm for spatial data such as satellite images. Integrating this functionality with artificial intelligence algorithms for analyzing spatial data enables us to predict challenging disasters such as deforestation. Deforestation as an environmental problems has been recorded the most serious threat to environmental diversity and one of the main components of land-use change. In this paper, we investigate spatial distribution of deforestation using artificial neural networks and satellite imagery. We modeled deforestation process using various factors in determining the relationship between deforestation and environmental and socioeconomic factors. Hence, for this purpose, the proximity to roads and habitats, fragmentation of the forest, height from sea level, slope, and soil type are considered in the model. In this research, we modeled land cover changes (forests) to predict deforestation using an artificial neural network due to its significant potential for the development of nonlinear complex models. The procedure involves image registration and error correction, image classification, preparing deforestation maps, determining layers, and designing a multi-layer neural network to predict deforestation. The satellite images for this study are of a region in Hong Kong which are captured from 2012 to 2016. The results of the study demonstrate that neural networks approach for predicting deforestation can be utilized and its outcomes show the areas that destroyed during the research period.

In this article, rst we are going to review the concept of ordinary frames , in more general case in measure spaces, namely, gc-frames. We try to develop the use of measure space in describing frames. Then by means of the gc-frames, we shall introduce gn-operators, which we shall show that each trace class operator has a vector-valued integral representation and vice-versa.

Early detection of breast cancer, one of the most common cancers in women worldwide, raises survival rates and lowers the cost of treatment. Although breast cancer detection and classification (CAD) tools have improved, some issues and restrictions still require further research. The creation of breast cancer CAD systems, particularly with deep learning models, was significantly impacted by recent advancements in machine learning and image processing techniques. The current deep learning-based CAD system to identify and categorize masses in mammography is presented in a structured manner in this survey. The survey provides the most popular breast cancer CAD system evaluation metrics, publicly available mammographic datasets, and current image modalities. The survey examines the available literature, emphasizing its strengths and limitations. The survey also emphasizes the difficulties and drawbacks of the current methods for classifying and detecting breast cancer. We point out research gaps and make suggestions for further study. This systematic review may be beneficial for clinicians using CAD systems to diagnose breast cancer early on and for researchers looking for knowledge gaps and making more contributions to the field.

In this paper, an all-fiber Mach-Zehnder interferometer (MZI) sensor for refractive index (RI) measuring is presented, which is based on Multimode–Single-mode–Multimode (MSM) fiber. The effects of both reducing the radius of the sensing part and the surface plasmon resonance (SPR) on its efficiency are investigated. Increasing the interaction of high-order modes with external media, caused by etching the cladding layer of the single-mode fiber part, significantly improves the sensitivity. Both wavelength and intensity interrogation approaches are employed to study the Multimode–etched Single-mode–Multimode (MESM) fiber sensor. The intensity and the wavelength sensitivities for the RI measurement in the range of 1.428-1.458 are obtained as -2308.92 %/RIU and 1313.14 nm/RIU, respectively. Finally, the MESM-SPR sensor is proposed and characterized. Results exhibit high performance in the RI range of 1.333 to 1.357, in which the sensitivity of 1433 nm/RIU is achieved. The advantages like low cost, high sensitivity, and simple fabrication methods make these sensors promising devices for chemical, food industry, and biosensing applications.

Strain induced melt activation process (SIMA) creates a globular microstructure which improves the hardness and ultimate tensile stress (UTS) of 7075 aluminum alloys. But, at the grain boundaries of SIMA processed 7075 aluminum alloy, presence of continuous and brittle intermetallic compounds and the eutectic structure result in decreasing mechanical properties, such as elongation, formability and etc. Hence, in order to improve microstructure and formability of 7075 aluminum alloy respect to SIMA process, a new process was done that is called two-step strain induced melt activation (TSSIMA). This process, which has been mentioned in the experimental procedure section, both organized globular structure and significantly modified the microstructure of the alloy compared to that of the SIMA method. It promoted discontinuity and more homogeneity in distribution of the precipitates and, approximately removed the eutectic mixture. Performing the rolling process on the alloy also revealed that it is more effective in formability enhancement in comparison with SIMA process. Also, it increased the hardness of 7075 aluminum alloy respect to that of SIMA process.

Containing hundreds of spectral bands (features), hyperspectral images (HSIs) have high ability in discrimination of land cover classes. Traditional HSIs data processing methods consider the same importance for all bands in the original feature space (OFS), while different spectral bands play different roles in identification of samples of different classes. In order to explore the relative importance of each feature, we learn a weighting matrix and obtain the relative weighted feature space (RWFS) as an enriched feature space for HSIs data analysis in this paper. To overcome the difficulty of limited labeled samples which is common case in HSIs data analysis, we extend our method to semisupervised framework. To transfer available knowledge to unlabeled samples, we employ graph based clustering where low rank representation (LRR) is used to define the similarity function for graph. After construction the RWFS, any arbitrary dimension reduction method and classification algorithm can be employed in RWFS. The experimental results on two well-known HSIs data set show that some dimension reduction algorithms have better performance in the new weighted feature space.

The rapid increase in population along with the economic activities led to rapid depletion of natural resources. Land use studies help us analyze the impacts of urban development on environment. Given the political upheavals in Afghanistan, this study aims to analyze how urban development evolved from 1978 to 2018 in six major cities- Kabul, Kandahar, Kunduz, Herat, Mazar-e Sharif and Jalalabad- in Afghanistan using Landsat Satellite Images. This study is based on quantitative approach. ArcGIS 9.4 software was used to synchronize the Landsat Satellite Images within the area of study. The results of the study show that the Annual rate of urban land expansion in Afghanistan was the lowest (average 1.07 square kilometers per year) during the military presence of Soviet Union in Afghanistan while it was the highest (3.35 square kilometers per year) from 2001 to 2018 due to the military presence of US-led NATO forces, relative security and rapid economic activities in Afghanistan. The authors believe that this study could be further explored if other inter-connected factors, e.g., the role of culture, literacy, immigration etc., are incorporated into the study of urban development processes in Afghanistan.

The chlorophyll is one of the most important natural pigments used extensively in the food industry. Two important factors for the production of chlorophyll are the use of plants rich in chlorophyll and efficiency of extraction method. Present investigation was performed to compare the extraction of photosynthetic pigments by using solvents of different chemical nature. The purslane plants with different growth behavior viz. Scrollable and standing were grown under shade and sunshine stress condition. Different solvents including diethyl ether, 5% ethanol, pure acetone, 20% acetone, pure methanol and 10% methanol were used to extract chlorophyll and carotenoids from the purslane plant. The results indicated that stress, growth type and different solvents had a significant effect on the extraction of chlorophyll and carotenoids. Different trend was observed in extraction rate for chlorophylls and carotenoids. Among the solvents, pure methanol was the best for extraction of chl a. Methanol and acetone were appropriate solvents to achieve the highest amount of chlorophyll from plant tissues. Among different solvents, pure methanol for chl a, pure acetone and methanol for carotenoids were best solvent for purslane plant with a growing type scrollable of under shade.

Due to the broadening of services from industrial enterprises to customers, the industrial network has gained more significance than what is really thought. As a part of this revolution, this paper tried to introduce the best suitable routing option for the healthcare, banking and internet services industries to optimize the routing department of their enterprises. RIP is not associated with this study, being out of favor in the industries due to hop count limitation. The idea behind doing such a thing is to avoid improper implantation of routing inside the enterprises which causes lack of optimization in routing department. The method which is selected for this paper is qualitative case study of banking, healthcare and internet services industries with unstructured interviews of IT experts of the respected industries in an open-ended questions fashion. The results were illustrated based on the stories and the discussion of the stories is the main contributor to the result of the study. The evaluation results of the stories have illustrated, that EIGRP is a suitable routing option for health care and banking, while OSPF can be suitable for the internet services industry. The implication of the study can be optimized network of industries such as healthcare, banking and internet services and it introduces the best suitable routing option for the demanded industries with the case study method and narrative analysis of the stories, which start from load balancing type and ends to demanded routing protocol, while reaching to demanded routing protocol, it introduces the best suitable routing option of that industry

Turbidity is an important water quality parameter, especially for drinking water. The ability to actively monitor the turbidity level in Drinking Water Distribution Systems is of critical importance to the safety and wellbeing of the public. Traditional turbidity monitoring methods involve manual collection of water samples at set locations and times followed by laboratory analysis, which are labor intensive and time consuming. Fiber-optic measurement permits real-time, in-situ turbidity monitoring. But the current technology is based on plastic fibers, which suffer from high optical attenuation and hence are unsuitable for large-scale remote monitoring. In this paper, we report the demonstration of a fiber-optic turbidity sensor based on multi-mode glass fibers. The system uses a single fiber to both deliver laser light into the water sample and collect the back-scattered light for detection. A balanced-detection scheme is utilized to remove the common-mode noise to enhance the turbidity sensitivity. Highly linear turbidity responses are obtained and a turbidity resolution as low as 0.1 NTU is achieved. The test unit is also shown to have excellent reproducibility against repeated measurements and good stability against temperature changes. Turbidity measurement in real environmental matrices such as tap water and pond water is also reported with an assessment of the impact of flow rate. This work demonstrates the feasibility of future large scale distributed fiber-optic turbidity monitoring networks.

The fatty-acid profiles of five main commercial pistachio cultivars, including Ahmad-Aghaei, Akbari, Chrok, Kalle-Ghouchi and Ohadi, were determined by gas chromatography: palmitic (C16:0), palmitoleic (C16:1), stearic (C18:0), oleic (C18:1), linoleic (C18:2), linolenic (C18:3) arachidic (C20:0) and gondoic (C20:1) acid. Based on the oleic to linoleic acid (O/L) ratio, a quality index was determined for these five cultivars: Ohadi (2.40) < Ahmad-Aghaei (2.60) < Kale-Ghouchi (2.94) < Chrok (3.05) < Akbari (3.66). Principal component analysis (PCA) of the fatty-acid data yielded three significant PCs, which together account for 80.0% of the total variance in the data set. A linear discriminant analysis (LDA) model evaluated with cross validation correctly classified almost all samples: the average percent accuracy for the prediction set was 98.0%. The high predictive power for the prediction set shows the ability to indicate the cultivar of an unknown sample based on its fatty-acid chromatographic fingerprint.

This article reports the two-step synthesis of NiFeO_x nanomaterials, their characterisation and bifunctional electrocatalytic activity measurements in alkaline electrolyte for metal-air batteries. The samples were mostly in layered double hydroxide at the initial temperature, but upon heat treatment, they were converted to NiFe₂O₄ phases. The electrochemical behaviour of the different samples was studied by linear sweep voltammetry and cyclic voltammetry on the glassy carbon electrode. The OER catalyst activity was observed for low mass loadings (0.125 mg cm⁻²), whereas high catalyst loading exhibited the best performance on the ORR side. The sample heat treated at 250 °C delivered the highest bi-functional oxygen evolution and reduction reaction activity (OER/ORR) thanks to its thin-holey nanosheet like structure with higher nickel oxidation state at 250 °C. This work further helps to develop low-cost electrocatalyst development for metal-air batteries.

Nowadays industrial dryers are used instead of traditional methods for drying. In designing dryers suitable for controlling the process of drying and reaching a high quality product, it is necessary to predict the instantaneous moisture loss during drying. For this purpose, ten mathematical-experimental models with a neural network model based on the kinetic data of pistachio drying are studied. The data obtained from the cabinet dryer will be evaluated at four temperatures of inlet air and different air velocities. The pistachio seeds will be placed in a thin layer on an aluminum sheet on a drying tray and weighed by a scale attached to the computer at different times. In the neural network, data are divided into three parts: educational (60%), validation (20%) and test (20%). Finally, the best mathematical-experimental model using genetic algorithm and the best neural network structure for predicting instantaneous moisture are selected based on the least squared error and the highest correlation coefficient.

Boiler efficiency is called to some extent of total thermal energy which can be recovered from the fuel. Boiler efficiency losses are due to four major factors: the dry gas flux, the latent heat of steam in the flue gas, the combustion loss or the loss of unburned fuel, radiation and convection losses. In this research, the thermal behavior of boilers in gas refinery facilities is studied and their efficiency and their losses are calculated. The main part of this research is comprised of analyzing the effect of various parameters on efficiency such as excess air, fuel moisture, air humidity, fuel and air temperature, the temperature of combustion gases, and thermal value of the fuel. Based on the obtained results, it is possible to analyze and make recommendations for optimizing boilers in the gas refinery complex using response-surface method (RSM).

Greenhouse Gases (GHGs) emission has considerable impact on global warming and climate change. Since energy systems and their features noticeably influence on the amount of GHGs emission, it can be modeled based on the specifications of energy sources utilized by the countries. In addition, economic activity is another factor which should be considered in GHG emission modeling. In this work, Artificial Neural Network (ANN) is used for estimating carbon dioxide emission, as one of the most abundant GHGs, on the basis of shares of various energy sources used as primary energy supply and GDP as an indicator for economic activities. Five countries including Iran, Kuwait, Qatar, Saudi Arabia and United Arab Emirates (UAE) are considered as case studies. Comparing between the estimated data by the achieved model and actual quantities showed acceptable precision of the ANN model for prediction of carbon dioxide emission. The average absolute relative error and the R-squared values of the GMDH model are approximately 2.28% and 0.9998, respectively. The obtained values for the mentioned statistical criteria show the precision of the model in forecasting the emission of Co2.

Increase in the emission of Greenhouse Gases (GHS) is among the significant concerns of government, societies, and policymakers. Due to the highest share of carbon dioxide in the produced GHGs, it is necessary to assess the factors that influence its emission. Energy systems and economic activities noticeably influence the amount of carbon dioxide production of countries. In this article, Artificial Neural Network (ANN) in addition to a linear correlation used to predict carbon dioxide emission of four CIS countries, including Turkmenistan, Uzbekistan, Kazakhstan, and Azerbaijan based the consumption of various energy sources and GDP, as the economic indicator. According to the obtained data by the proposed models, carbon dioxide emission can be accurately estimated by utilizing the mentioned input data. Models’ R-squared value are 0.9997 and 0.9999 in the cases of applying the correlation and ANN-based model. Moreover, the average absolute relative deviations by utilizing the correlation and GMDH ANN are approximately 1.05% and 0.61%, respectively. These statistical values demonstrate more proper performance of the ANN-based model compared with the applied linear correlation.

Load variations in any power system result in losses escalation and voltage drops. With the sensible and optimal allocation of distributed generators (DGs), these problems could be considerably mitigated. It has been seen in the priorly existing methods that ideally allocation of DGs has been carried out during fixed loads and constant power requirements. However, in real scenarios the loads are always variable and allocation of DGs must be done in accordance to the variations of the connected load. Therefore, the current paper addressed the aforementioned problem by distinctive optimal allocation of DGs for each variability of 24hour load horizon. However, the single exclusive solution is considered among all allocations of 24 hours. The min-max regret concept has been utilized in order to deal with such methodology. Altogether, 24 scenarios are analyzed wherein each scenario corresponds to a specific hour of the respective day. The optimal allocation of DGs in terms of their optimal sizing and placement has been carried out by using three algorithms including battle royal optimization (BRO), accelerated particle swarm optimization (APSO) and genetic algorithm (GA). The multi-objective optimization problem is evaluated on the basis of minimum value criterion of the multi-objective index (MO). MO comprises of active and reactive power losses and voltage deviation. Hence, in order to find robustness of proposed technique, Conseil international des grands réseaux electriques (CIGRE’s) MV benchmark model incorporating 14 buses has been considerably used as a test network. In the end the results of three proposed algorithms have been compared.

Due to low working temperature, high energy density and low pollution, proton exchange fuel cells have been investigated under different operating conditions in different applications. Using platinum catalysts in methanol fuel cells leads to increasing the cost of this kind of fuel cell which is considered as a barrier to the commercialism of this technology. For this reason, a lot of efforts have been made to reduce the loading of the catalyst required on different supports. In this study, carbon black (CB) and carbon nanotubes (CNT) have been used as catalyst supports of the fuel cell as well as using the double-metal combination of platinum-ruthenium (PtRu) as anode electrode catalyst and platinum (Pt) as cathode electrode catalyst. The performance of these two types of electro-catalyst in the oxidation reaction of methanol has been compared based on electrochemical tests. Results showed that the carbon nanotubes increase the performance of the micro-fuel cell by 37% at maximum power density, compared to the carbon black. Based on thee-electrode tests of chronoamperometry and voltammetry, it was found that the oxidation onset potential of methanol for CNT has been around 20% less than CB, leading to the kinetic improvement of the oxidation reaction. The current density of methanol oxidation reaction increased up to 62% in CNT sample compared to CB supported one, therefore the active electrochemical surface area of the catalyst has been increased up to 90% by using CNT compared to CB which shows the significant rise of the electrocatalytic activity in CNT supported catalyst. Moreover, the resistance of the CNT supported sample to poisonous intermediate species has been found 3% more than CB supported one. According to the chronoamperometry test results, it was concluded that the performance and sustainability of the CNT electro-catalyst show remarkable improvement compared to CB electro-catalyst in the long term.

The negative implications of climate change for human health are now well-established. Yet these have not been fully considered into climate change communication strategies. Research suggests that reorienting climate change communication with a health frame could be a useful communication strategy. We conducted a long-term and broad overview of existing scientific literature in order to summarize the state of research activity in this area, by extent and by nature. The methodology is based on a scoping review of scientific articles published on climate change communication and health between 1990 and mid-2016 indexed in the PubMed, ScienceDirect, and Web of Science databases. The screened citations were reviewed for inclusion and data were extracted and coded in order to conduct quantitative (e.g. frequencies) and qualitative (i.e. content analysis) analyses.Out of 2,866 identified published papers, only 24 articles were eligible for analyses. The main themes identified were effective communication of climate change (n=10, 41.7%), the role of health professionals (n=10, 41.7%) and the perception of climate change (n=4, 16.7%). We identified a large proportion of secondary research articles (n= 15, 62.5%) including reviews (n=5, 20.8%) and opinion articles (n=10, 41.7%). A significant share - 37.57% (n=9) - of the identified articles were classified as original research articles, suggesting that the number of publications in this area - particularly original research - has not grown rapidly.This scoping review identified several themes including effective communication of climate change, the role of health professionals, and the perception of climate change in the selected articles on the subject. The research literature on the communication of climate change and health is relatively recent and emerging: the first articles on the subject were published from 2008 onward only.

In network analysis, links depict the connections between each pair of network nodes. However, such pairwise connections fail to consider the interactions among more agents, which may be indirectly connected. Such non-pairwise or higher-order connections can be signified by involving simplicial complexes. The higher-order connections become even more noteworthy when it comes to neuronal network synchronization, an emerging phenomenon responsible for the many biological processes in real-world phenomena. However, involving higher-order interactions may considerably increase the computational costs. To confound this issue, map-based models are more suitable since they are faster, simpler, more flexible, and computationally more optimal. Therefore, this paper addresses the impact of pairwise and non-pairwise neuronal interactions on the synchronization state of 10 coupled memristive Hindmarsh-Rose neuron maps. To this aim, electrical, inner linking, and chemical synaptic functions are considered as 2- and 3-body interactions in three homogenous and two non-homogenous cases. The results show that through chemical pairwise and non-pairwise synapses, the neurons achieve synchrony with the weakest coupling strengths.

Aim: The aim of this study was to assess the performance of the SF-36 health survey (SF-36) in a sample of subjects with physical disabilities. Material and Methods: 305 patients recruited using the convenient sampling method from September 2019 to March 2020 in Kermanshah, Iran. Another similar 300 patients were selected to assess the confirmatory factor analysis (CFA). Results: The Cronbach’s α ranged from 0.70 to 0.93, and intra-class correlation coefficients from 0.71 to 0.88; and with the no ceiling and floor effect for two main subscales. Convergent validity was supported by moderate to good correlation between SF-36 subscales and Moorong self-efficacy subscales (r= 0.25- 0.53). The SF-36 divergently validated with HADS total score and subscales (r= -0.24- -0.65), concurrently validated with its subscales (r=0.49- 0.88), and physically discriminated between persons with different level of disability (t-test: p<0.001). Factorial analysis identified seven factors, confirmed with second-order in another 300 samples (chi-square (χ2/df) = 2.61(p < .001); RMSEA = 0.07 (90% CI = 0.07–0.08); AGFI=0.75; GFI = 0.78; CFI= 0.85; and NFI = 0.78). Conclusions: the SF-36 is a reliable and valid tool in physical disables. However, SF-36 shows insufficient eight-factor validity. Future studies should focus on evaluating other psychometric properties, such as sensitivity to change in subjects with physical disabilities.

Nowadays, one of the biggest concern of human being is greenhouse gas emission, especially carbon dioxide emission in developed and under-developing countries. In this study, connectionist models including LSSVM (Least Square Support Vector Machine) and evolutionary methods are employed for predicting the amount of CO₂ emission in six Latin American countries, i.e., Brazil, Mexico, Argentina, Peru, Chile, Venezuela, and Uruguay. The studied region is modelled based on the available input data in terms of Million tons including oil (Million tons), gas (Million tons oil equivalent), coal (Million tons oil equivalent), $R_{ew}$ (Million tons oil equivalent), and Gross Domestic Product (GDP) in terms of billion US dollars.} Moreover, the available patents in the fields of climate change mitigation in six Latin American countries namely Brazil, Mexico, Argentina, Peru, Chile, Venezuela, and Uruguay has been reviewed and analyzed. The results show that except Venezuela, all other mentioned countries have invested in renewable energy R&D activities. Brazil and Argentina have the highest share of renewable energies which account for 60% and 72% respectively.

Solar energy is a renewable resources of energy which is broadly utilized and have the least pollution impact between the available alternatives of fossil fuels. In this investigation, machine leaening approaches of neural networks (NN), neuro-fuzzy and least squares support vector machine (LSSVM) are used to build the models for prediction of the thermal performance of a photovoltaic-thermal solar collector (PV/T) by estimating its efficiency as an output of the model while inlet temperature, flow rate, heat, solar radiation, and heat of sun are input of the designed model. Experimental measurements was prepared by designing a solar collector system and 100 data extracted. Different analyses are also performed to examine the credibility of the introduced approaches revealing great performance. The suggested LSSVM model represented the best performance regarding the mean squared error (MSE) of 0.003 and correlation coefficient (R²) value of 0.99, respectively.

Although different assessments and evaluations of Dual-Miller cycle performed, specified output power and thermal performance associated with engine determined. Besides, multi objective optimization of thermal efficiency, Ecological Coefficient of performance ( ) and Ecological function ( ) by the mean of NSGA-II technique and thermodynamic analysis performed. The Pareto optimal frontier obtaining the best optimum solution is chosen by fuzzy Bellman-Zadeh, LINMAP, and TOPSIS decision-making techniques. Based on the results, performances of dual-Miller cycles and their optimization are improved.

Acute intense exercise causes significant oxidative stress and consequently an increase in total antioxidant capacity; however, the mechanisms and combined effects of intense exercise and smoking on oxidative stress among active and non-active smokers are not clear. The aim of this study was to investigate the effect of acute intense exercise on antioxidant enzyme activity responses in active and non-active individuals exposed to cigarette smoke. The study included 40 subjects who were equally classified as: smokers that did exercise (SE), smokers that did not do exercise (SnE), non-smokers that did exercise (NSE), and non-smokers that did not do exercise (NSnE). The adjusted Astrand test was used to exhaust the subjects. Salivary enzymes of peroxidase (POX), catalase (CAT), and superoxide dismutase (SOD) were measured, by spectrophotometry methods, at 3 different time points: pre-test (TP1), post-test (TP2), and one hour after finishing the test (TP3). Significant (p<0.05) group x time interactions were found for the three enzymes. Salivary POX, CAT and SOD increased in all groups from TP1 to TP2 and decreased from TP2 to TP3. Only the NSE showed a significant difference between TP1 to TP3 in POX and SOD by +0.011 ± 0.007 and +0.075 ± 0.02 (U/ml), respectively. The NSE showed significantly higher levels of POX, CAT and SOD in TP2 compared to the other groups. Furthermore, NSE and NSnE had higher levels of POX, CAT and SOD in TP1 and TP3 (p<0.05) compared with SE and SnE. Only in the NSnE, were no differences observed in CAT compared with SE and SnE in TP3. These results showed that the antioxidant level at rest and in the recovery time after the acute intense exercise was lower in SE and SnE compared with NSE and NSnE, suggesting that smoking habit may reduce the ameliorating effect of regular physical activity on acute exercise-induced oxidative stress.

Solar energy is a renewable resource of energy that is broadly utilized and has the least emissions among the renewable energies. In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the inputs variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced approaches and evaluate their performance. The proposed LSSVM model outperformed ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.

(1) Background: COVID-19 had devastating effects on both physical and mental well-being, prompting the need for interventions. This study aimed to explore the impact of mindfulness-based stress reduction on mental health, mindfulness, and sleep disorders in COVID-19 survivors.; (2) Methods: In this interventional parallel study, the sample was selected university students in nursing and midwifery faculties using total population sampling. The intervention included providing counseling on mindfulness methods by trained experts. General health, mindfulness and sleep quality questionnaires were used for data collection. Data were analyzed using SPSS22, descriptive and interferential statistic. A significance level of 0.05 was considered; (3) Results: In this study, the mean age of the students was 20.29 ± 2.03 years. 69.5% of the participants were female and 96% were single. 71.3% were studying nursing and 67% had a history of Corona infection. The intervention was only effective on general health questionnaire and the mean score of this questionnaire was 1.7 higher than the control group; (4) Conclusions: the study indicated that MBSR intervention improves mindfulness, mental health, and sleep quality in COVID-19 survivors. Regular assessment and use of this intervention can help address long-term challenges and improve overall well-being.

Introduction: Circulating tumor DNA (ctDNA) is newly diagnosed tumor DNA that can easily represent a tumor’s genetic and epigenetic change. Pheochromocytomas (PCCs) and Paragangliomas (PGLs) are rare tumors of adrenal gland tissue that have the potential to be detected by ctDNA. We aimed to study the potential of the methylation status of RDBP, SDHB, and SDHC genes in ctDNA of PCCs/PGLs patients as a diagnostic biomarker. Materials and Methods: Clinical data, fresh frozen tissue, the blood of 12 PCCs/PGL patients, and the blood of 12 age/sex-matched normal patients were collected. The methylation status of RDBP, SDHB, and SDHC was compared between cases and controls by MS-HRM analysis. Results: Amongst six promoter regions of RDBP, SDHB, and SDHC, promoter methylation quantification of SDHCa and RDBPb was significantly different between PCCs/PGLs and controls. SDHCa was methylated in 49.93% of PCCs/PGLs cases vs. 8.33 % of control samples, p-value: 0.026, area under curve AUC=0.757, and RDBPb in 74.9% of PCCs/PGLs cases vs. 25.0% of control samples, p-value: 0.032, AUC=0.750. Conclusions: This study suggests the ctDNA potential for a less invasive source of tumor epigenetic modification in PCCs/PGLs malignancies. The SDHCa and RDBPb hypermethylation warrant further exploration as diagnostic tools for PCCs/PGLs.

Extracellular vesicles (EVs) are enclosed by a lipid-bilayer membrane and secreted by all types of the cells under various physiological conditions. EVs play important roles in intercellular communication and crosstalk between tissues in the body. They are classified into three groups, such as apoptotic bodies, microvesicles, and exosomes. Exosomes were isolated from biofluids including blood, urine, milk, and cell culture media. Exosomes have significant potential for drug delivery and diagnosis. However, the method of isolation affects the physical and biological properties of exosomes. Several methods based on different principles have been developed for exosome isolation. These include ultrafiltration, precipitation, ultracentrifugation, size-exclusion chromatography, and microfluidics. In this study, we applied three common methods, such as ultrafiltration, precipitation, and ultracentrifugation, to isolate exosomes from the cell culture medium and investigated the effects of these different isolation methods on the size distribution and quality of the isolated exosomes. Field emission scanning electron microscopy (FESEM) images, size distribution, total protein content, and the effect of exosomes on the viability of hypoxic cells were analyzed in this study. The analysis revealed that compared to other methods, the ultracentrifugation method can isolate exosomes with smaller diameter (ranging from 20 to 80 nm), lower total protein content (50 µg/ml), and causing the increased viability of the hypoxic cells. The precipitation method does not require special equipment and is inexpensive, if the quality and purity of this method are solved, and it can be used as the best method for exosome isolation. This study can serve as a guide for choosing the best exosome isolation method for applications in medicine according to the needs, time, cost, and equipment.

Background: There are ongoing workforce challenges with the delivery of long-term care (LTC), such as staffing decisions based on arbitrary standards. The Synergy tool, a resident-centered approach to staffing, pro-vides objective, real-time acuity and dependency scores (Synergy scores) for residents. The purpose of this study was to implement and evaluate the impact of the Synergy tool on LTC delivery. Methods: A longitudinal mixed methods study took place within two publicly-funded LTC homes in British Columbia, Canada. Quantitative data included weekly Synergy scores for residents (24 weeks), monthly aggregated resident falls data (18 months) and a six-month economic evaluation. Qualitative data were gathered from family caregivers and thematically analyzed. Results: Quantitative findings from Synergy scores revealed considerable variability for resident acuity/dependency needs within and across units; and falls decreased during implementation. The six-month economic evaluation demonstrated some cost savings by comparing Synergy tool training and implementation costs with savings from resident fall rates reductions. Qualitative analyses yielded three positive impacts themes (improved care delivery, better communication, and improved resident-family-staff relationships), and two negative structural themes (language barrier and staff shortages). Conclusions: The Synergy tool provides useful data for enhancing a ‘fit’ between resident needs and available staff.

Studies suggest that elevated postnatal blood lead levels (BLLs) are negatively associated with child growth. This study aimed to investigate associations of childhood BLLs at age one-year and growth outcomes at age six-years (n=661) in a cohort of children in Allada, Benin. The growth outcomes studied are weight-for-age Z-score (WAZ), height-for-age Z-score (HAZ) and BMI-for-age Z-score (BMIZ), head circumference (HC), underweight, stunting, and wasting. Multivariable regression models examined associations between blood lead levels, growth outcomes with adjustment for potential confounders. The geometric mean BLLs was 59.3 μg/L and 82% of children had BLLs &gt;35 μg/L at one year. After adjusting for confounding factors, no association was found between BLLs quartiles and HAZ, WAZ, BMIZ, and height and weight growth velocities. However, boys in the highest quartile had 1.02 cm lower HC (95% CI: [-1.81, -0. 24]) as compared to those in the lowest quartile with a dose-response trend across quartiles (Ptrend =0.02). Furthermore, an increased risk of being stunted was observed in children in the highest quartile of exposure compared to the first (OR: 2.43; 95% CI: [1.11 – 5.33]) with a dose-response trend (Ptrend =0.03). Blood lead was found to be associated with an increased risk of childhood stunting and lower head circumference in a resource-limited setting.

Lead exposure is associated with poor cognitive development in children. Very few studies in sub-Saharan Africa (SSA) have studied blood lead levels (BLLs) and non-gasoline sources of exposure in children. Data from a birth cohort in Benin (2011-2013) suggested that 58% of one-year-old children had BLLs > 50 ug/L. We aimed to investigate the prevalence of elevated BLLs (>50 µg/L and >100 µg /L) among 425 of these children at six-years-of-age in 2016-18 and to compare BLLs between age one-year and six-years and study sources of lead at six years. BLLs were analyzed by inductively coupled plasma mass spectrometry. Multiple linear regression and quantile regressions were used to study potential sources of lead. The prevalence of BLLs >50 µg/L in children was 59.5% [Geometric Mean (GM) 56.4 µg/L, 95% CI: 54.1 - 58.7] at six years of age compared to 54.8% [GM 56.5 µg/L, 95% CI: 53.4-59.6] at one year of age. The prevalence of children with BLLs >100 µg/L decreased from 14.4% at one year of age to 8.2% at six years of age. After adjustment for all other covariates, consumption of peanut more than once per month was significantly associated with a 22.0% (95% CI: 4.6, 42.5) increment in BLLs at six years compared with no consumption. Consumption of bushmeat killed by lead bullets at six years was associated with an increase in the higher percentiles of BLLs (P75) compared with the absence of this source. Other potential sources of lead associated with BLLs with marginal significance were consumption of rice, paternal occupational exposure, and the presence of activity with the potential use of lead. This prospective cohort confirms the persistently high prevalence of elevated BLLs in children residing in a rural region in the south of Benin as well as the presence of multiple and continuous sources of lead. These results highlight the need for prevention programs to reduce and eliminate lead exposure in children.

This paper presents Tiltification, a multi modal spirit level application for smartphones. The non-profit app was produced by students in the master project “Sonification Apps” in winter term 2020/21 at the University of Bremen. In the app, psychoacoustic sonification is used to give feedback on the device’s rotation angles in two plane dimensions, allowing users to level furniture or take perfectly horizontal photos. Tiltification supplements the market of spirit level apps with the unique feature of auditory information processing. This provides for additional benefit in comparison to a physical spirit level and for more accessibility for visu- ally and cognitively impaired people. We argue that the distribution of sonification apps through mainstream channels is a contribution to establish sonification in the market and make it better known to users outside the scientific domain. We hope that the auditory display community will support us by using and recommending the app and by providing valuable feedback on the app functionality and design, and on our communication, advertisement and distribution strategy.

Rhabdomyosarcoma is a rare cancer arising in skeletal muscle that typically impacts children and young adults. It is a worldwide challenge in child health as treatment outcomes for metastatic and recurrent disease still pose a major concern for both basic and clinical scientists. The treatment strategies for rhabdomyosarcoma include multi-agent chemotherapies after surgical resection with or without ionization radiotherapy. In this comprehensive review, we first provide a detailed clinical understanding of rhabdomyosarcoma including its classification and subtypes, diagnosis, and treatment strategies. Later we focus on chemotherapy strategies for this childhood sarcoma and discuss the impact of three mechanisms that are involved in chemotherapy response including apoptosis, macro-autophagy, and the unfolded protein response. Finally, we discuss in vivo mouse and zebrafish models, and in vitro 3-dimensional bioengineering models of Rhabdomyosarcoma to screen future therapeutic approaches and promote muscle regeneration.