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Abstract Identifying accident prone areas and the contributing environmental factors can save lives and enhance infrastructure durability. This research presents an innovative use of two hierarchical clustering methods (Agglomerative Hierarchical and BIRCH clustering algorithms) to detect accident hotspots with high accident rates on the Yazd-Kerman road, in Iran. These approaches identified clusters of accidents, highlighting significant clusters and categorizing their overlap as two accident prone areas. The high percentage of overlapping results from both methods indicates a high level of consistency in the findings. Through observations, field visits, police report analysis, and interviews with locals, the primary causes of accidents in accident prone areas were identified. In one of the accident prone areas, the most significant reasons for accidents included the presence of a resting area, insufficient lighting at curves, and poor road signage, which created a dilemma zone for drivers. In another accident prone area, the main contributing factor was reduced visibility during dust storms. Then, K-Nearest Neighbors and Random Forests machine learning algorithms were employed to predict the severity of accidents, using various input attributes such as lighting, climate, alignment slope, and road geometry. The K-Nearest Neighbor surpassed the Random Forest technique, achieving an overall accuracy of 71% in contrast to 60%. This study effectively evaluated the outcomes of clustering, uncovering the underlying causes of accidents to inform future practical interventions. Moreover, by predicting the severity of accidents along the road, a framework was developed to propose strategies for risk reduction.

Abstract In the present study, the photocatalytic performance of ZnS/TiO2 nanocomposite was investigated through the photodegradation of Acid Blue 113 (AB113) dye under ultraviolet light exposure. TiO2 and ZnS-based nanocomposites suffer from relatively wide bandgap energy and low adsorption capacity which limit their photocatalytic applications. These problems can be suppressed by modifying the surface of nanocomposite particles by the non-thermal plasma. Herein, surface modification of the ZnS/TiO2 nanocomposite was performed using a dielectric-barrier discharge plasma under nitrogen (N2) and tetrafluoromethane (CF4) gases. The characteristics of the plasma-treated nanocomposites were evaluated by XRD, FTIR, Raman, FESEM, EDS, BET, BJH, and DRS analyses. According to the results, by applying plasma treatment, cation and anion vacancies are produced that reduces the band gap energy of the photocatalyst hence improves its performance. The results indicate that the photocatalytic efficiency of the N2-plasma-treated nanocatalyst has been almost two times higher than that of the untreated ZnS/TiO2. It was found that after 25 min of UV irradiation, the AB113 was almost completely degraded in the presence of N2-plasma-treated ZnS/TiO2 nanocomposite (about 95%), whereas, it was degraded by 64% and 46% in the presence of CF4-plasma-treated ZnS/TiO2 and untreated ZnS/TiO2, respectively. This study presents a new approach to designing cost-effective plasma-treated photocatalysts to degrade organic contaminants in wastewater.

Abstract To successfully implement low-salinity polymer flooding in heterogeneous heavy oil reservoirs, it is crucial to comprehend the interactions between salinity, polymer properties, and reservoir characteristics. Artificial intelligence-driven proxy models can assist in identifying critical parameters and predicting performance outcomes, thereby enabling optimizing field-scale applications of this technique in heterogeneous heavy oil reservoirs. This study focused on developing a proxy model by coupling neural network and particle swarm optimization algorithms to analyze low-salinity polymer flooding. The model, trained with data from a pilot-scale dynamic simulator, achieved high predictive accuracy, featuring a regression value of 0.996 and a mean square error of 0.0011. It effectively forecasts key performance indicators such as oil recovery, water cut, and well bottom-hole pressure. The model identified injection rate as the most influential factor and polymer concentration as the least significant. Through the optimization of input parameters, the study established optimized values for the injection rate, injected fluid salinity, and polymer concentration at 1450 (bbl/day), 4000 ppm, and 1500 ppm, respectively. The optimization considers economic viability by maximizing net present value and addresses practical challenges of maintaining injectivity over time, making it a valuable tool for enhancing water-based recovery methods in oil field development.

Abstract In this randomized controlled trial, we assessed the effects of three consecutive intra-silicone oil (SO) injections of methotrexate (MTX) on the outcomes of surgery for proliferative vitreoretinopathy grade C (PVR-C). Seventy-four eyes of 74 patients with PVR-C were included. Of these, 37 eyes were assigned to the MTX group and 37 eyes to the control group. Fourteen patients failed to comply with the 6-month follow-up period. All eyes underwent vitrectomy and SO injection. In the MTX group, 250 µg MTX was injected into the SO after surgery and at weeks 3 and 6 postoperatively. The primary outcome was the retinal reattachment rate at 6 months. The secondary outcomes included limited PVR recurrence and adverse events. Retinal reattachment was achieved in 22 eyes (73.3%) in the MTX group and 23 eyes (76.7%) in the control group (difference: -3.4%, 95% CI: -25.2–18.5%). Limited PVR recurrence was observed in one eye (4.5%) in the MTX group versus nine eyes (39.1%) in the control group at 6 months (P = 0.01). No adverse effects were observed. Adjunctive treatment with three consecutive applications of intra-SO MTX did not reveal a significant effect on the retinal re-detachment rate but could statistically significantly reduce limited PVR recurrence. Trial registration: http//ClinicalTrials.gov NCT04482543, 22/07/2020.

Abstract This study concentrates on the implementation of Marine Predator Algorithm (MPA) scheme for tuning of a power system stabilizer’s (PSS’s) parameters to damp the low-frequency oscillations in a power system. To this, the single machine infinite bus system (SMIB), the Western System Coordinating Council (WSCC) and the New England 10 machine 39-bus power system are utilized for testing and comparing different metaheuristic algorithms using different fitness functions. Optimal PSS parameters of SMIB test system are validated using CU-SLRT Std, a real-time digital simulator. The comparative studies demonstrate that the MPA optimized PSS yields improvements of up to 98.62% in the Particle Swarm Optimization (PSO) at 69.42%, Whale Optimization Algorithm (WOA) at 71.79%, Flower Pollination Algorithm (FPA) at 72.39%, African vulture optimization algorithm (AVOA) at 78.04%, Wild Horse Optimization (WHO) algorithm at 68.57% under various operating scenarios. The superiority of the MPA optimized PSS has been validated using Hardware-in-the-loop implementation for the SMIB test system.

Abstract This paper proposes a new energy management system to combine Fuel Cells (FC) and photovoltaic (PV) panels as primary power sources. Also, battery and Super Capacitor (SC) banks are considered as secondary energy systems. The high power density and the fast dynamics of SCs combined with the high energy density and medium dynamics of batteries would be an ideal combination for FC vehicles. The high step-up DC/DC converter is used along with the implementation of the maximum power point tracking algorithm (MPPT) to adapt the low power of PV panels with the high power of DC-Link. Also, an evolutionary optimization algorithm is proposed along with the FOPI controller to improve the output of the energy management algorithm and control the sources more efficiently. The simulation results are validated for a standard urban driving cycle. It indicates for the case of the sustained presence of a PV source during a typical driving cycle, the source delivers about 55% of its maximum power.

Abstract Kidney stones or Nephrolithiasis are the most common health condition associated with the urinary system. Dietary factors stand as important factors in the occurrence and development of kidney stones. This study aimed to examine the potential link between dietary insulin index (DII) and dietary insulin load (DIL) with prevalence of kidney stones. This cross-sectional study was conducted among adults aged 30 to 75 years in the Shahedieh district of Yazd, Iran, over the period of 2015–2016. DII and DIL were calculated using a validated semi-quantitative food-frequency questionnaire and mathematical formula. Diagnosis of kidney stones is made on the basis of information obtained from self-reported questionnaire (Yes/ No). To explore the association between DII and DIL with the odds of kidney stones, logistic regression was employed in crude and adjusted models. A total of 4,829 participants were included in this study. Individuals in the last quartile of DIL had 214% higher odds of kidney stones in the crude model (OR: 2.14, 95% CI: 1.62–2.83; P-trend

Abstract The Internet of Medical Things (IoMT) is poised to play a pivotal role in future medical support systems, enabling pervasive health monitoring in smart cities. Alzheimer’s disease (AD) afflicts millions globally, and this paper explores the potential of electroencephalogram (EEG) data in addressing this challenge. We propose the Convolutional Learning Attention-Bidirectional Time-Aware Long-Short-Term Memory (CL-ATBiLSTM) model, a deep learning approach designed to classify different AD phases through EEG data analysis. The model utilizes Discrete Wavelet Transform (DWT) to decompose EEG data into distinct frequency bands, allowing for targeted analysis of AD-related brain activity patterns. Additionally, the data is segmented into smaller windows to handle the dynamic nature of EEG signals, and these segments are transformed into spectrogram images, visually depicting brain activity distribution over time and frequency. The CL-ATBiLSTM model incorporates convolutional layers to capture spatial features, attention mechanisms to emphasize crucial data, and BiLSTM networks to explore temporal relationships within the sequences. To optimize the model’s performance, Bayesian optimization is employed to fine-tune the hyperparameters of the ATBiLSTM network, enhancing its ability to generalize and accurately classify AD stages. Incorporating Bayesian learning ensures the most effective model configuration, improving sensitivity and specificity for identifying AD-related patterns. Our model extracts discriminative features from EEG data to differentiate between AD, Mild Cognitive Impairment (MCI), and healthy controls (CO), offering a more comprehensive approach than existing two-class detection algorithms. By including the MCI category, our method facilitates earlier identification and potentially more impactful therapy interventions. Achieving a 96.52% accuracy on Figshare datasets containing AD, MCI, and CO groups, our approach demonstrates strong potential for practical use, accelerating AD identification, enhancing patient care, and contributing to the development of targeted treatments for this debilitating condition.

Abstract The primary cause of anemia worldwide is due to poor diet and iron deficiency. Iron (Fe) enriched yeast can be the most effective way to manage anemia because of the capability for biotransformation of mineral to organic and bioavailable iron. To overcome the low richness of yeast, the use of siderophore as cellular iron carriers is a new approach. In this research, for the first time the potential of siderophore in increasing the Fe enrichment of Saccharomyces boulardii (S. boulardii), which is important because of its probiotic properties and resistance to different stresses, has been investigated to produce of potential iron supplements. For this purpose, siderophore was produced by Pseudomonas aeruginosa (P. aeruginosa). Siderophore impact, along with ten other independent process variables, has been studied on the efficiency of iron biotransformation by the Plackett–Burman design (PBD). The results showed that the highest biotransformation yield was 17.77 mg Fe/g dry cell weight (DCW) in the highest biomass weight of 9 g/l. Iron concentration is the most important variable, with contributions of 46% and 70.79% for biomass weight and biotransformation, respectively, followed by fermentation time, agitation speed, and KH2PO4 concentration. But increasing the level of siderophore and zinc led to a significant negative effect. siderophore inefficiency may be attributed to the absence of membrane receptors for pyoverdine (Pvd) and pyochelin (Pch) siderophores. Also, the steric hindrance of the cell wall mannan, the stickiness and sediment ability of the yeast, can create limitations in the absorption of elements. Such yeast can be used as a potential source of iron even for vegetarians and vegans in the form of medicinal and fortified food products to improve the treatment of anemia. It is recommended that further research be focused on increasing the iron enrichment of yeast by overcoming the structural barrier of the cell wall, investigating factors affecting membrane permeability and iron transport potential of other types of siderophores.

Abstract Despite low incidence, neuroblastoma, an immunologically cold tumor, is the most common extracranial solid neoplasm in pediatrics. In relapsed/refractory cases, the benefits of autologous hematopoietic stem cell transplantation (auto-HSCT) and other therapies are limited. Natural killer (NK) cells apply cytotoxicity against tumor cells independently of antigen-presenting cells and the adaptive immune system. The primary endpoint of this trial was to assess the safety of the injection of allogenic, ex vivo-expanded and primed NK cells in relapsed/refractory neuroblastoma patients after auto-HSCT. The secondary endpoint included the efficacy of this intervention in controlling tumors. NK cells were isolated and primed ex vivo (by adding interleukin [IL]-2, IL-15, and IL-21) in a GMP-compliant CliniMACS system and administered to four patients with relapsed/refractory MYCN-positive neuroblastoma. NK cell injections (1 and 5 × 107 cells/kg in the first and second injections, respectively) were safe, and no acute or sub-acute adverse events were observed. During the follow-up period, one complete response (CR) and one partial response (PR) were observed, while two cases exhibited progressive disease (PD). In follow-up evaluations, two died due to disease progression, including the case with a PR. The patient with CR had regular growth at the 31-month follow-up, and another patient with PD is still alive and receiving chemotherapies 20 months after therapy. This therapy is an appealing and feasible approach for managing refractory neuroblastomas post-HSCT. Further studies are needed to explore its efficacy with higher doses and more frequent administrations for high-risk neuroblastomas and other immunologically cold tumors. Trial registration number: irct.behdasht.gov.ir (Iranian Registry of Clinical Trials, No. IRCT20201202049568N1).

Abstract In the current investigation, the effect of recycled steel fibers recovered from waste tires on the pull-out response of ribbed steel bars from carbon nanotube (CNT)-modified ultrahigh performance fiber reinforced concrete (UHPFRC) was considered using the multiscale finite element method (MSFEM). The MSFEM is based on three phases to simulate CNT-modified UHPC, recycled steel fibers (RSFs), and ribbed steel bars. For the first time, a bar ribbed has been simulated to make more realistic assumptions, and RSFs have been distributed in the form of curved cylinders of different lengths and with a random distribution within a concrete matrix. The interaction of the steel bar and the RSFs with the concrete is applied by the cohesive zone model (CZM). After confirming the simulation outcomes with the experimental results, the steel bar pull-out response is investigated using load-slip curves. The impact of the CNT content, RSFs and their aspect ratio on the bond strength of steel bars and CNT-modified UHPFRC was assessed. The results show that using RSFs with a lower aspect ratio (steel microfibers) significantly improves the pull-out characteristics of steel bars from concrete. Accordingly, the proposed MSFEM is considered for simulating the effects of different parameters on the pull-out response of ribbed steel bars from concrete without causing complex, time-consuming, or costly experiments. The results indicated that waste fiber or RSF can be used as a toughening component in CNT-modified ultrahigh-performance concrete and as a replacement for industrial steel fibers.

Abstract In river research, forecasting flow velocity accurately in vegetated channels is a significant challenge. The forecasting performance of various independent and hybrid machine learning (ML) models are thus quantified for the first time in this work. Utilizing flow velocity measurements in both natural and laboratory flume experiments, we assess the efficacy of four distinct standalone machine learning techniques—Kstar, M5P, reduced error pruning tree (REPT) and random forest (RF) models. In addition, we also test for eight types of hybrid ML algorithms trained with an Additive Regression (AR) and Bagging (BA) (AR-Kstar, AR-M5P, AR-REPT, AR-RF, BA-Kstar, BA-M5P, BA-REPT and BA-RF). Findings from a comparison of their predictive capabilities, along with a sensitivity analysis of the influencing factors, indicated: (1) Vegetation height emerged as the most sensitive parameter for determining the flow velocity; (2) all ML models displayed outperforming empirical equations; (3) nearly all ML algorithms worked optimal when the model was built using all of the input parameters. Overall, the findings showed that hybrid ML algorithms outperform regular ML algorithms and empirical equations at forecasting flow velocity. AR-M5P (R2 = 0.954, R = 0.977, NSE = 0.954, MAE = 0.042, MSE = 0.003, and PBias = 1.466) turned out to be the optimal model for forecasting of flow velocity in vegetated-rivers.

Abstract Diabetes mellitus (DM) is among the most widespread non-communicable diseases and poses a substantial global health challenge. The aim of this study was to examine the incidence of DM and its nutritional, anthropometric, laboratory, demographic, and behavioral determinants, as well as comorbidities, within a Kurdish population residing in western Iran. This research was conducted in the Ravansar Non-Communicable Disease (RaNCD) cohort study, followed 9170 participants aged 35–65 years, for an average ± SD of 7.11 ± 1.26 years, from 2015 until 2023. A hierarchical Cox regression model was used to estimates the adjusted hazard ratios (HRs). The incidence of DM was 4.45 (95% CI 3.96, 4.99) per 1000 person-years. We found several significant predictors for DM incidence, including prediabetes, comorbidity, urban residence, total antioxidant capacity (TAC), and the interaction between gender and body mass index (BMI). Prediabetes emerged as the strongest predictor of DM incidence, with a hazard ratio of 10.13 (CI 7.84, 13.09). Additionally, having two diseases (HR = 2.18; 95% CI 1.44, 3.29) or three and more diseases (HR = 3.17; 95% CI 2.06, 4.90) increased the risk of developing DM. Also, the hazard ratios for the effects of gender on DM incidence in the normal, overweight, and obese BMI groups were 0.24, 0.81, and 1.01, respectively. The presence of prediabetes and obesity serve as the crucial indicators for the onset of DM, emphasizing the pressing need for interventions to prevent DM in these circumstances. Furthermore, there are notable disparities between urban and rural populations in this study, warranting further investigations to ascertain the underlying causes of such variations.

Abstract The educational environment plays a vital role in the development of students who participate in athletic pursuits both in terms of their physical health and their ability to detect fatigue. As a result of recent advancements in deep learning and biosensors benefitting from edge computing resources, we are now able to monitor the physiological fatigue of students participating in sports in real time. These devices can then be used to analyze the data using contemporary technology. In this paper, we present an innovative deep learning framework for forecasting fatigue in athletic students following physical exercise. It addresses the issue of lack of precision computational models and extensive data analysis in current approaches to monitoring students’ physical activity. In our study, we classified fatigue and non-fatigue based on photoplethysmography (PPG) signals. Several deep learning models are compared in the study. Using limited training data, determining the optimal parameters for PPG presents a significant challenge. For datasets containing many data points, several models were trained using PPG signals: a deep residual network convolutional neural network (ResNetCNN) ResNetCNN, an Xception architecture, a bidirectional long short-term memory (BILSTM), and a combination of these models. Training and testing datasets were assigned using a fivefold cross validation approach. Based on the testing dataset, the model demonstrated a proper classification accuracy of 91.8%.

Abstract This article presents an innovative approach using the Differential Transform Method (DTM) to analyze the vibration characteristics of cylindrical shells, integrating Taylor's series with Sander's classical theory. It demonstrates DTM's efficiency, accuracy, and potential as an alternative method. The study introduces a novel application of the DTM in exploring the free vibration of cylindrical shells, detailing a technique to address challenges such as normalization, linear solution methodologies, and parameter derivative modifications. A dimensionless parameter analysis evaluates the impact of length, radius, thickness, and modulus of elasticity. Comparative analysis with Hybrid Finite Element Method (FEM) data and validation against existing literature highlights DTM's precision and reliability. In conclusion, DTM offers a robust solution for the eigenvalue problem in coupled differential equations, providing accurate vibration parameters. Additionally, an important relationship between the modulus of elasticity and frequency in the dimensionless state was obtained.

Abstract In the present work, the inhibition performance of Viola extract based on bulk and nano size as a green corrosion inhibitor on mild steel in 0.5 M phosphoric acid and 1M hydrochloric acid solutions is investigated using different techniques (potentiodynamic polarization, electrochemical impedance spectroscopy (EIS) and Optical microscopy). The gained results demonstrated that various concentrations of Viola Extract (bulk and nano) inhibited the corrosion of the alloy in both of the acid solutions. The temperature impact on corrosion rate without/with this extract was examined. Certain thermodynamic parameters were determined based on the temperature impact on inhibition and corrosion processes. The adsorption mechanism of the extract on the alloy was explored using the Langmuir adsorption isotherm. A mixed mode of adsorption was observed, wherein the nano-sized extract in 1.0 M HCl predominantly underwent chemisorption, while the bulk-sized extract in 1.0 M HCl and both bulk and nano-sized extracts in 0.5 M H3PO4 were primarily subjected to physisorption. Scanning electron microscopy (SEM) and Optical microscopy analyses were employed to scrutinize alloys’ surface morphology.

Abstract The reservoir’s properties are required for proper reservoir simulation, which also involves uncertainties. Experimental methods to estimate the relative permeability and capillary pressure data are expensive and time-consuming. This study aims to determine the relative permeability and capillary pressure functions of a sandstone core in the presence and absence of clay during low-salinity water floods. The data were provided by automatic history matching the results from previously lab-reported studies through coupling a simulator with the particle swarm optimization algorithm. Correlations were proposed using multiple-linear regression for relative permeability and capillary pressure parameters at low-salinity conditions. They were validated against experimental results of no clay and clayey formation with regression of 95% and 97%. To assign one curve of relative permeability and capillary pressure to the grid cells of the simulator, averaging techniques were implemented. The effect of salinity and clay content on the obtained curves was investigated. Changing salinity from 42000 to 4000 ppm, the reduction in water relative permeability appeared to be higher than the oil relative permeability increment. Moreover, a noticeable shift in the relative permeability curves toward the highest saturations related to the clay content was observed. The proposed hybrid method could be a suitable tool to estimate the relative permeability and capillary pressure functions of the water-based EOR methods.

Abstract Zeolite imidazolate framework-8 (ZIF-8), which is a special subgroup of metal–organic frameworks (MOFs), was synthesized and modified by ethylenediamine (ZIF-8-EDA) to prepare an efficient adsorbent for the high sorption of Cd2+ ions from solution. The synthesized and modified ZIF-8 (ZIF-8-EDA) were characterized by X-ray diffraction (XRD), Fourier-transform infrared (FT-IR) spectroscopy, Brunauer–Emmett–Teller (BET), field emission scanning electron microscopy (FE-SEM) with energy dispersive spectroscopy (EDS), and transmission electron microscopy (TEM) analysis. The optimum conditions for dosage of adsorbent, initial ion concentration, pH, and contact time were 0.05 g/l, 50 mg/l, 6, and 60 min, respectively, for cadmium ion sorption from aqueous solutions with a removal efficiency of 89.7% for ZIF-8 and 93.5% for ZIF-8-EDA. Adsorption kinetics and equilibrium data were analyzed using the Langmuir and Freundlich equations. The Langmuir model fitted the equilibrium data better than the Freundlich model. According to the Langmuir equation, the maximum uptake for the cadmium ions was 294.11(mg/g). The calculated thermodynamic parameters (ΔG°, ΔH°, and ΔS°) indicated that the adsorption process was feasible, spontaneous, and endothermic at 20–50 °C. Based on the results, the amino functionalized ZIF-8 had improved adsorption performance due to the replacing of the starting linker with organic ligands that had effective functional groups, leading to chemical coordination due to the interaction of metal ions with the non-bonding pair of electrons on the N atoms of the amino functional group. The selectivity toward metal ion adsorption by ZIF-8-EDA was Cd2+ > Pb2+ > Ni2+‏.

Abstract The associations of vitamin D receptor (VDR)- single nucleotide polymorphisms (SNPs) with the symptoms of COVID-19 may vary between patients with different severities of COVID-19. Therefore, in the present study, we aim to compare VDR polymorphisms in severe and mild COVID-19 patients. In this study, a total number of 85 hospitalized patients and 91 mild/moderate patients with COVID-19 were recruited. SNPs in VDR genes were determined using ARMS and then confirmed by sanger sequencing. The mean (SD) age of participants in hospitalized and non-hospitalized group was 59.0 (12.4) and 47.8 (14.8) years, respectively. Almost 46% of participants in hospitalized and 48% of participant in non-hospitalized group were male. The frequency of TT genotype of SNP rs11568820 was significantly lower in hospitalized than non-hospitalized group (3.5% vs. 17.6%; P = 0.018). However, there was no significant differences between genotypes of SNPs rs7970314 and rs4334089 and also alleles frequencies in all SNPs of two groups. The genotype of rs11568820 SNP had an inverse association with hospitalization of patients with COVID-19 after adjustment for comorbidities [OR 0.18, 95% CI 0.04, 0.88; P = 0.034]. While, there was no relationship between genotypes of SNPs rs7970314 and rs4334089 and hospitalization. The TT genotype of rs11568820 plays protective role in sever COVID-19 and hospitalization. Further studies with a large sample size which consider various confounding factors are warranted to confirm our results.

Abstract Promotion of public health is one of the most important benefits of ecosystems. Nevertheless, the relationship between ecosystems and social health’ needs is not well understood. Therefore, a study was done to investigate the potential of natural (forests and rangelands) and artificial (urban parks and gardens) ecosystems in ensuring the five dimensions of public health (i.e. physical, mental, spiritual, social and environmental) in the social systems (urban and rural societies). Therefore, 47 health indicators were used in order to relate different ecosystems and social’ needs to five dimensions of public health through questionnaire. The results indicated that natural ecosystems had the greatest potential in providing mental, spiritual and environmental health due to ecological characteristics of wilderness and aesthetic. The artificial ecosystems had the greatest potential in providing physical and social health due to their easy access. However, there was a match between social health’ needs and ecosystem potential in the rural areas. The study highlighted the need for promotion of ecological indicators related to mental health in urban areas by enhancing silence and aesthetic in artificial ecosystems. Presented framework can provide comprehensive information on the weaknesses and strengths of different ecosystems to promote public health based on social needs and fixing the weaknesses of artificial ecosystems in urban areas.

Abstract Contact resistance, as one of the main parameters that limits the performance of graphene-based transistors, is highly dependent on the metal-graphene contact fabrication processes. These processes are investigated and the corresponding resistances are measured based on the transfer length method (TLM). In fabrication processes, when annealing is done on chemical vapor deposition (CVD)-grown graphene samples that are transferred onto SiO2/Si substrates, the adhesion of graphene to the substrate is improved, and poly methyl methacrylate (PMMA) residues are also reduced. When the metal deposition layer is first applied to the graphene, and then, the photolithography process is performed to define the electrodes and graphene sheet, the graphene-metal contact resistance is better than that in other methods due to the removal of photoresist residues. In fact, by changing the sequence of the fabrication process steps, the direct contact between photoresist and graphene surface can be prevented. Thus, the contact resistance is reduced and conductivity increases, and in this way, the performance of graphene transistor improves. The results show that the fabrication process has a noticeable effect on the transistor properties such as contact resistance, channel sheet resistance, and conductivity.‌ Here, by using the annealing process and changing the order of photolithography processes, a contact resistance of 470 Ω μm is obtained for Ni-graphene contact, which is relatively favorable.

Abstract This study aims to investigate the trends and project the major risk factors of Non-communicable Diseases (NCDs) in Iran. We obtained the trend of prevalence of main risk factors related to NCDs in 30 to 70-year-old-individuals. The data were extracted from WHO STEP wise approach to NCDs risk factor surveillance (STEPS) survey. Also,the previous studies conducted at national and subnational levels from 2001 to 2016 were employed. The prevalence of risk factors was projected by 2030 using Bayesian Model Averaging (BMA) and Spatio-temporal model stratified by sex and province. The percent change for the age-standardized prevalence of smoking in men between 2001 and 2016 was calculated to be − 27.0. Also, the corresponding values for the risk factors of diabetes, hypertension, obesity and overweight, physical inactivity (PI), and mean of salt intake were − 26.1, 29.0, 70.0, 96.8, 116.6, and 7.5, respectively. It is predicted that smoking and these risk factors will undergo a change to show values of − 1.26, 38.7, 43.7, 2.36, and 15.3 by 2030, respectively. The corresponding values in women for the time interval of 2001–2016 were − 27.3, 26.3, 82.8, 1.88, 75.2, and 4.2, respectively. Plus, projections indicate that the 2030 variation values are expected to be − 25.0, 16.7, 37.5, 28.7, 26.7, and 10.9 respectively. This study showed that the prevalence of four risk factors of PI, overweight and obesity, hypertension, and diabetes is increasing in Iran. Therefor, it is necessary to carry out effective interventions to adopt a healthy lifestyle and reduce the risk factors.