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Abstract Background Effective diabetic retinopathy screening (DRS) programmes are important in preventing vision impairment and blindness caused by diabetes. This study focuses on identifying the factors affecting attendance or non-adherence to DRS among children and young adults with diabetes mellitus (DM) in Bangladesh. Methods A mixed-methods approach was used, which included patients diagnosed with DM aged 12–26 years from Bangladesh who were registered at BIRDEM Women and Children hospital in Dhaka. Data collection occurred between July 2019 and July 2020, mainly through telephone and email due to restrictions imposed by the COVID-19 pandemic. Statistical analyses, including chi-squared tests, t-tests, and logistic regression, were used to assess the demographic and clinical factors influencing attendance at DRS. Results The study reported a high 88% attendance rate for DRS among children and young adults in Bangladesh. However, some barriers to attendance were identified. Children under 15 years of age showed a higher tendency to attend their last DRS appointment when compared to older age participants (16–26 years), P

The growing interest in combining the photocatalytic properties of semiconductors like ZnO and TiO2 with the superior electron conduction capabilities of graphene has resulted in the successful synthesis of in-situ reduced graphene oxide (rGO) supported ZnO-TiO2 nanostructures through a simple microwave-assisted synthesis method. X-ray Diffraction Spectroscopy (XRD), Field Emission Scanning Electron Microscope (FESEM), UV–visible spectroscopy (UV–vis), and Fourier Transform Infrared Spectroscopy (FTIR) were employed to characterize structural, morphological and optical properties as well as surface functional groups of the synthesized products. The XRD measurements of our synthesized samples confirm both structural crystallinity and phase purity, while the FTIR analysis verifies the complete reduction of graphene oxide (GO) to reduced graphene oxide (rGO). The synthesized ternary nanocomposite ZnO-TiO2-rGO exhibited a remarkable 100 % adsorption-assisted removal efficiency for 20 mg/L methylene blue (MB) dye under ultraviolet light illumination within 120 min, along with a 56 % dye adsorption removal efficiency in the same time interval. In comparison, pure ZnO showed 0 % adsorption and only 31 % photocatalytic efficiency at the similar condition. Remarkably, the ZnO-TiO2-rGO nanocomposite exhibited exceptional photocatalytic activity mediated by adsorption, achieving complete degradation of MB dye within 5 min under sunlight irradiation. The photocatalytic efficiency and dye adsorption capacity were found to be significantly lower for the anionic dye methyl orange (MO) compared to the cationic MB dye. The study thoroughly investigated the influence of catalyst dose and initial dye concentration on photodegradation. The proposed mechanism indicates that the extensive surface area and numerous active sites on the rGO promote adsorption, which is then followed by degradation through the metal oxides. Overall, the results unveil that the microwave-assisted synthesis of ZnO-TiO2-rGO nanocomposite is a promising and environmentally friendly approach for efficiently degrading dyes from contaminated wastewater using both UV light and natural sunlight irradiation.

Bismuth oxyfluoride (BiOF) is an emerging class of material with notable chemical stability, unique layered structure and striking energy band structure. Bi-based semiconductor materials and reduced graphene oxides (rGOs) have attracted considerable attention due to their broad spectrum of potential applications. Herein, we successfully synthesised an efficient photocatalyst comprising BiOF–rGO nanocomposites with embedded Ag nanoparticles using a simple hydrothermal method. The synthesised nanocomposites were characterised through Fourier-transform infrared spectroscopy, X-ray diffraction (XRD), field emission scanning electron microscopy and ultraviolet (UV)–visible spectroscopy. The XRD results indicated the crystalline structures of the BiOF, Ag-doped BiOF and Ag-doped BiOF–rGO composites. Photocatalytic activity assessments focused on the degradation of methylene blue (MB) and methyl orange (MO) dyes under UV-light and sunlight irradiation. The Ag-doped BiOF–rGO composite exhibited significantly enhanced degradation efficiency, achieving 61.81 % and 74.25 % degradation of MB and MO, respectively, after 300 min under UV-light irradiation. On the contrary, pure BiOF demonstrated only 17.63 % and 48.29 % degradation for MB and MO, respectively, under similar conditions. Furthermore, under sunlight irradiation, the Ag-doped BiOF–rGO composite exhibited an MB removal efficiency of 43.87 % after 300 min, whereas pure BiOF showed only 27.47 % under identical conditions. These results underscore the potential of Ag-doped BiOF–rGO composites as highly efficient and adaptable photocatalysts for the photodegradation of organic dyes in industrial wastewater.

Agriculture has a crucial role in both the economy and food supply. However, the frequent occurrence of plant diseases can have a significant negative influence on the production of foods. Timely detection of a disease is important for its effective management, e.g., targeted use of pesticides. Conventional Plant Disease Detection (PDD) methods are manual, slow, tedious, and prone to errors, thereby increasing the risk of significant yield losses. Recently, various data-driven approaches, including Deep Learning and Computer Vision-based approaches, have been explored for PDD. However, the scarcity of large and annotated datasets and the limited scalability of these approaches have prompted researchers to turn to the Self-Supervised Learning (SSL) approach. In this review, we provide the very first conceptual grounding for the SSL approach in PDD. By reviewing a large body of recent related works in the literature, we thoroughly analyse and categorise them into generative, predictive, contrastive and hybrid SSL models. Moreover, this review analyses various recent datasets and performance metrics used in these models. Finally, we explain the research challenges and key research directions aimed at advancing PDD through the SSL approach.

The democratic welfare government is equally committed to quality-driven, impartial, and egalitarian education. The major contribution of this study is to examine the problems and opportunities posed by the incorporation of digital technology in the classroom via the lens of Classroom 4.0 (CLSR4) and education 4.0. The commitment of the democratic welfare government to equitable and quality education is investigated, in line with the Global Agenda SDG 4 goal of ensuring comprehensive and lifelong education for everyone. The modern classroom is witnessing a clash between traditional teaching methods and the digital competency of today’s tech-savvy generation. The goal of this study is to examine the connections between education 4.0 and classroom 4.0 by looking at developments in classroom architecture and the use of digital platforms. The biggest issue found is instructors’ inability to adjust to changing conditions. In terms of methodology, confirmatory factor analysis is used in this work to investigate the dynamics of correlations in the context of Classroom 4.0. This method offers insights into the variables influenced by metaverse technology, resulting in a thorough comprehension of the evolving educational scene. This study’s findings shed insight into how students see the changing classroom and the role that various platforms play in engaging them. The study underscores the need for educators to effectively integrate these tools and address this digital transformation to improve teaching and learning experiences. However, there are study constraints, most notably the limited contextual scope, which is exclusively focused on the Indian educational setting. Further research in other worldwide contexts is required to generalize the findings. In the future, it is critical to investigate and build creative instructional practices that effectively synergize with Classroom 4.0. Furthermore, further research should be conducted to determine how governments and educational institutions might adapt to this paradigm shift, ensuring a smooth and effective transition into the digital era of education.

This research paper examines the digital infrastructure required to achieve project management success by analyzing the enabling elements of this digital infrastructure in terms of three pillars: digital readiness, digital fitness, and digital tools. A comprehensive literature review was conducted to identify these enabling elements and to develop a list of project management success indicators through which the success of project management can be measured. To evaluate and rank the digital infrastructure enabling elements, a Multi-Criteria Analysis (MCA) was implemented using a hybrid approach combining Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS). The study used the digital infrastructure enabling elements as MCA alternatives and the project management success indicators identified in the literature review as MCA criteria. The results indicated that the enabling elements associated with digital tools are the most significant for project management success, with a FTOPSIS closeness coefficient (CCi) of 0.8525, followed by those related to digital fitness (CCi = 0.6481) and digital readiness (CCi = 0.1602). These findings have proven to be robust, as they remained consistent even when weights of the MCA criteria were adjusted in three new scenarios proposed in a scenario analysis. This research highlights the critical role of digital enabling elements in enhancing project management practice and achieving project management success. It also offers a strategic framework for organizations to develop and strengthen their digital infrastructure.

The main goals of a successful education should consider students’ mental health and academic achievement. To overcome obstacles and setbacks in the classroom, the learners must be equipped with self-aid strategies. Despite the associations between affection control, the foundation of self-evaluation, and theoretical floatability, no research in psycholinguistics has ever revealed their connections. In order to do this, the current study tested a structural model of the affection control, self-evaluation, and theoretical floatability of EFL university learners. 155 Saudi EFL university students were given the affection control questionnaire (ACQ), self-evaluation questionnaire (SEQ), and the theoretical floatability scale (TFS). The results showed that learners’ theoretical floatability can be predicted by affection control and self-evaluation based on structural equation modeling (SEM). It was also proven that self-evaluation contributed to theoretical floatability. The findings have implications for increasing learners’ realization of their personality features and self-evaluation, which can promote practical evaluation.

Based on a sociocultural theory and a plethora of experiential studies on the efficacy of scaffolding on foreign language learning, the goal of this research article is to examine the effectiveness of diverse types of scaffolding on university students’ lexical digesting and reading perception from a sociocultural standpoint. Using the Harvard Placement Test, 40 EFL students from a total of 70 were selected and divided into two experimental groups and a single control group. The first group (G1 Exp.) was given teaching with instructor scaffolding, the second group (G2 Exp.) received instruction with peer scaffolding, and the control group (G3 Cont.) received conventional instruction without any external enhancement. Pretests for lexical digesting and reading perception were administered to the three groups. Following the treatment, post-tests for lexical digesting and reading perception were administered. Descriptive statistics and inferential statistics were used to examine the collected data. The results showed a significant difference between instructor and peer scaffolding, with the peer scaffolding group outperforming the instructor scaffolding group. Both experimental groups outperformed the control group in lexical digesting and reading perception. Regarding various kinds of scaffolding, this study provided some advice for psycholinguists, curriculum designers, and language instructors.

The radio frequency (RF) data generated from a single-chip millimeter-wave (mmWave) ground-based multi-input multi-output (GB-MIMO) radar can provide a highly robust, precise measurement for deformation in harsh environments, overcoming challenges such as different lighting and weather conditions. Monitoring deformation is significant for safety factors in different applications, such as detecting and monitoring the ground stability of underground mines. However, radar images can experience different types of clutter and artifacts besides the spreading effects caused by the side lobes, resulting in the foremost challenge of suppressing clutter and monitoring deformation.In the state of the art, the introduced frameworks usually include many filters proposed for different types of noise, with commercial systems typically using an amplitude threshold. This paper proposes a framework for monitoring the deformation, where the essential process is to apply a data-driven threshold to the amplitude heatmap, detect the deformation, and eliminate noise. The proposed threshold is an iterative approach based on radar imagery statistics, and it performs well for the collected dataset. The principal advantage of our proposed framework is simplicity, reducing the burden of using different filters. We can consider the dynamic threshold based on data statistics as a data-driven machine learning tool. The results show promising performance for our method in monitoring the deformation and removing clutter compared to the benchmark method.

A positive flow cytometry crossmatch (FCXM) due to donor specific antibodies (DSA) constitutes a risk for kidney transplantation; such a finding may indicates an unacceptable donor for this patient. However, positive FCXM in the absence of DSA is considered discordant and need further investigations. During COVID-19 pandemic, we observed 22% discordant results out of 445 FCXM performed during eight months period in our laboratory and another 7% were invalid due to high background negative control (NC). No study has addressed the impact of COVID-19 pandemic on FCXM and the overall pre-kidney transplant workups or described a solution to deal with these non-specific reactivities. Herein, we analyzed all FCXM results in SARS-CoV-2 seropositive patients and addressed how this pandemic affected significantly the pre-kidney transplant workups, highlighting both technical and financial implications. We also shared our modified FCXM procedures using dithiotheritol (DTT) sera treatment or blocking donor cells with negative control human serum (NCS) which we found to be successful to abrogate 98% of all discordant FCXM results and to validate all invalid results due to high background NC.In conclusion, COVID-19 pandemic has affected our HLA laboratory significantly by creating many false positive or invalid crossmatch results. Transplant laboratories must consider this before test interpretations and immune risk assessments. We recommend the use of DTT serum treatment to remove nonspecific bindings in the sera of kidney transplant candidates and the use of NCS-blocked donor cells to correct high background when performing FCXM in transplant candidates or donors with recent history of SARS-CoV-2 immunization respectively.

Abstract Superhydrophilic/superhydrophobic patterned surfaces can be used to create droplet microarrays. A specific challenge with the liquids needed for various biomedical applications, as compared to pure water, is their lower surface tension and potential for contaminating the surfaces through adsorption. Here, a method is shown to create biofluid droplet microarrays using discontinuous dewetting of pure water, an oil protective layer, and finally biofluid exchange with the water droplet array. With this method, a droplet array of a viscous nucleic acid amplification solution can be formed with a low surface tension of 34 mN m−1 and a contact angle of only 76° with the used hydrophobic coating. This droplet array is applied for nucleic acid detection of SARS‐CoV‐2 virus using strand invasion‐based amplification (SIBA) technology. It is shown that by using an array of 10 000 droplets of 50 µm diameter the limit of detection is 1 RNA copy µL−1. The results demonstrate that SIBA on droplet microarrays may be a quantitative technology.

The paper proposes a mathematical model for long-term load forecast (LTLF) based on parametric and time series statistical techniques. The flowchart of the proposed algorithm was also presented. The multiple linear regression (MLR) as well as the autoregressive integrated moving average (ARIMA) model with different model orders are employed in load forecasting. Historical data from 1990 to 2022 were utilized in the model implementation and validation. The input data imply gross domestic product (GDP), oil prices, population, and the energy from renewable energy projects as independent variables, and the annual peak load is the dependent variable. The results obtained by MLR show that population and GDP have a positive impact on electricity demand, whereas the oil price and the penetration of the renewable energy have a negative impact on electricity demand. In ARIMA, the load forecast is estimated based on error (residual) estimation that is determined based on the time lag operator and autoregressive and moving average coefficients. The ARIMA (p, d, and q) model with six different model orders is investigated. The Bayesian information criteria (BIC) and Akaike information criteria (AIC) indices are used as a measure in the selection of the appropriate model for prediction. The comparison between the six model’s scenarios shows that ARIMA (1, 1, 1) is the best model that fits the time series with minimum error and with the lowest BIC and AIC. The RMSE, MAPE, MSE, and MAE provided by ARIMA (1, 1, 1) are 3.57%, 2.55%, 0.13%, and 54.36, respectively, whereas the AIC and BIC are 386.8 and 394.0, respectively. A comparison between ARIMA and MLR shows that ARIMA is better than MLR in terms of time series fitting and error level. The LTLF forecast period covering the period 2024–2035 considers three scenarios of future development in the country: the normal (medium forecast), the optimistic (high forecast), and the pessimistic (low forecast). The forecast is made in the three directions (i.e., medium, high, and low) to avoid the ambiguity and uncertainty that may occur in the data input used in forecasting the electricity demand. A comparison between the MLR and ARIMA in the last year 2035 shows a small deviation between the two methods with the forecasted values in the range between 5480 MW and 5520 MW, respectively.

ABSTRACTThree-dimensional (3D) reconstruction of a building can be facilitated by correctly segmenting different feature points (e.g. in the form of boundary, fold edge, and planar points) over the building roof, and then, establishing relationships among the constructed feature lines and planar patches using the segmented points. Present machine learning-based segmentation approaches of Light Detection and Ranging (LiDAR) point cloud data are confined only to different object classes or semantic labelling. In the context of fine-grained feature point classification over the extracted building roof, machine learning approaches have not yet been explored. In this paper, after generating the ground truth data for the extracted building roofs from three different datasets, we apply machine learning methods to segment the roof point cloud based on seven different effective geometric features. The goal is not to semantically enhance the point cloud, but rather to facilitate the application of 3D building reconstruction algorithms, making them easier to use. The calculated F1-scores for each class confirm the competitive performances over the state-of-the-art techniques, which are more than 95% almost in each area of the used datasets.

Abstract Increased attention has been directed toward utilizing visual modes because linguistic modes cannot accurately depict L2 learners’ metaphorical conceptions of their language-acquisition experiences. The goal of this study was to investigate the mental images that Saudi EFL students may have in mind while learning a foreign language. To this end, both verbal and nonverbal metaphorical conceptions were investigated. An experimental group of 12 students was randomly selected. Both the learners’ written responses and drawn descriptions were evaluated to tap into the learners’ mental representations of foreign language learning. The multimodality-based analysis of the upper-intermediate learners’ metaphors demonstrated that the EFL learners’ metaphorical verbal and non-verbal representations had the same perspectives on learning a foreign language. The learners’ positive attitudes were also revealed by the extracted metaphorical concepts. Most of the metaphorical representations showed language learning as an exciting and uniquely personal experience. Few metaphorical representations showed that learning English was an awful experience.

The paper introduces a statistical model that connects the electrical demand in Jordan with several determinants that have a direct impact on the electrical consumption and load profile during the study period from 2007 to 2020. The period was selected as it is characterized by several global events that directly impacted Jordan’s economy and energy sustainability in Jordan, such as the Arab spring protests, the civil war in Syria, and the global financial crises. Many determinants that are used in the regression analysis imply the ambient temperature, day of the week, population, gross domestic product (GDP), oil price, and technological factors related to renewable energy projects. Results show that temperature and population positively impact the demand, whereas GPD, population, oil prices, and renewable energy negatively impact the electricity demand. The results obtained from backcasting regression analysis for the hourly 4745 data set covering 13 years period reveals reasonable error metrics with MAE, MAPE, and RMSE values of 134, 6.3% and 2.76%, respectively. The government must encourage investments to exploit and explore the massive potential of available energy resources such as oil, natural gas, oil shale, and uranium to resolve the problems related to the high global oil prices and high dependency on imported energy. Also, it is required to enable the transition from fossil fuels to renewable energy through financial incentives and tax exemption to encourage investments in clean energy, rebuild a new traffic system showing the volatile electricity prices, which are still unknown and finally remove obstacles and facilitate the ongoing projects, reaching a state of stakeholder buy-in engaging with the projects.

Background Intracarpal injection of steroids has produced favorable effects in patients with carpal tunnel syndrome (CTS); however, it still carries some drawbacks. Perineural midazolam injection has some promising effects in chronic neuralgia. This study focused upon pain visual analog scale (VAS) improvement, and Boston Carpal Tunnel Syndrome Questionnaire (BCTQ) improvements when comparing intracarpal injection of midazolam versus dexamethasone.Methods One hundred and thirty-four patients with mild to moderate CTS were randomized (1:1 ratio) to receive intracarpal 3 ml bupivacaine 0.5% with either 8 mg dexamethasone in 2 ml saline (group DX) or 2 mg midazolam in 2 ml saline (group MZ). VAS and BCTQ were assessed preintervention and postintervention (1st week, 1st, 3rd, and 6th months)Results The VAS showed a significantly lower value in MZ than the DX group with median value of 2 (maximum-minimum = 1–3) in MZ and 4 (2–5) in DX by the 6th month p = 0.049. Intragroup comparison of follow-up VAS to the baseline value showed significant decreases in the MZ group during the whole study period, whereas in DX the decrease was noticed by the 1st week and 1st month only. In postinterventional BCTQ, both symptom severity (SSS) and functional severity (FSS) scores were significantly lower in MZ rather than DX group after the 1st, 3rd, and 6thmonths where SSS (p = 0.029, 0.048, 0.04) and FSS (p = 0.04, 0.019, 0.003) in consequence.Conclusions Intracarpal injection of midazolam offers a longer duration of pain relief and higher hand functional improvement scores in comparison to dexamethasone.

One of the most serious complications of snakebite is compartment syndrome (CS), Signs of CS include paresthesia in the distal part and pain that is out of proportion to the severity of the injury, Management involves improving the general condition, and fasciotomy has been found to be effective. An 8-year-old girl was referred to the internal emergency department in our hospital 72 h after a snake bite. She was unconscious, in poor general condition, and had a high fever. Edema was present throughout the right lower limb, extending to the abdomen, with signs of cellular necrosis (blackening with bubbles) on the right foot. The diagnosis of CS was made, and the patient underwent fasciotomy after improving the general condition. Although snake bites are rare, surgeons should be concerned about life-threatening and limb-threatening complications. CS can be effectively treated with fasciotomy. Additionally, it is important to emphasize the significance of daily dressing and regular follow-up for achieving the best results.

Background: Artificial intelligence (AI) appears capable of detecting diabetic retinopathy (DR) with a high degree of accuracy in adults; however, there are few studies in children and young adults. Methods: Children and young adults (3-26 years) with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) were screened at the Dhaka BIRDEM-2 hospital, Bangladesh. All gradable fundus images were uploaded to Cybersight AI for interpretation. Two main outcomes were considered at a patient level: 1) Any DR, defined as mild non-proliferative diabetic retinopathy (NPDR or more severe; and 2) Referable DR, defined as moderate NPDR or more severe. Diagnostic test performance comparing Orbis International’s Cybersight AI with the reference standard, a fully qualified optometrist certified in DR grading, was assessed using the Matthews correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), area under the precision-recall curve (AUC-PR), sensitivity, specificity, positive and negative predictive values. Results: Among 1274 participants (53.1% female, mean age 16.7 years), 19.4% (n = 247) had any DR according to AI. For referable DR, 2.35% (n = 30) were detected by AI. The sensitivity and specificity of AI for any DR were 75.5% (CI 69.7-81.3%) and 91.8% (CI 90.2-93.5%) respectively, and for referable DR, these values were 84.2% (CI 67.8-100%) and 98.9% (CI 98.3%-99.5%). The MCC, AUC-ROC and the AUC-PR for referable DR were 63.4, 91.2 and 76.2% respectively. AI was most successful in accurately classifying younger children with shorter duration of diabetes. Conclusions: Cybersight AI accurately detected any DR and referable DR among children and young adults, despite its algorithms having been trained on adults. The observed high specificity is particularly important to avoid over-referral in low-resource settings. AI may be an effective tool to reduce demands on scarce physician resources for the care of children with diabetes in low-resource settings.

The unparalleled availability of Satellite Image Time Series (SITS) for crop phenology classification unravels agricultural parcel observation and monitoring with applications of both economic and ecological importance. Moreover, the need for distinct classification of agricultural parcels into individual crop types falls on state-of-the-art deep learning models for this extrinsic task. However, most existing approaches implemented are complex and ineffective attention incorporated models, which in turn lack the resilience to recognize useful bands in achieving greater accuracy. We propose a Multi-Fast Channel Attention module for deep CNNs based on a Spatial Encoder (SE-MFCA) that requires a few parameters while enhancing the performance-complexity trade-off dilemma. Hence, we leverage on spatial encoder module to extract the images as disorderly sets of pixels to enhance the coarse spatial resolution features. We empirically show that appropriate parameter sharing in the cross channel interaction can preserve performance while significantly reducing model complexity. The proposed multi-channel attention module can efficiently be implemented via an encoder-decoder network to prevent the loss of detailed spatial information. Again, we parallelly distributed the input channel into multiple heads in our network to recover the specialized input features, which will concatenate with the residual to form a rich single feature representation. The extensive experiment has shown that our model SE-MFCA is efficient and effective compared with the previous state-of-the-art time series classification algorithm on a publicly available dataset of Sentinel-2 images for agricultural parcels. Performance-wise SE-MFCA achieves the highest overall accuracy of 94.50% and the highest mean intersection over union score of 51.92%, besides the least trainable params of 131 K and fewer floating point operations of 0.16 M.

Because healthcare systems in developing nations are underfunded and unequal, practitioners must understand how telemedicine may be widely introduced and sustained. Unfortunately, telemedicine adoption receives less attention and there is limited knowledge regarding the interfering influence of patient engagement, satisfaction, and individual innovativeness. Consequentially, the current study seeks to investigate the factors that influence patients’ behavioral intentions toward telemedicine, while acknowledging engagement and satisfaction as mediators and personal innovativeness as a moderator. A questionnaire instrument was used to reach telemedicine users in Bangladesh, and finally, 305 samples were curated and explored applying structural equation modeling in AMOS software. The empirical results show that information quality, performance expectancy, engagement, and satisfaction are strong stimulators of behavioral intention toward telemedicine, whereas functionality, information quality, and performance expectancy drive satisfaction and engagement. One of the worthy findings of this study is that engagement and satisfaction play a significant mediating function, while personal innovativeness plays a moderating role, in influencing behavioral intention toward telemedicine. Confirming engagement and satisfaction as mediators and innovativeness as a moderator adds originality to this research, and overall will help to reduce healthcare bias prejudice among marginalized people in developing countries like Bangladesh.

Abstract Despite their importance, dynamic and diagnostic assessments (DigAs) have received little attention in phycological aspects of foreign language teaching and learning settings; therefore, this study compared the effects of dynamic and diagnostic assessment (DigA) on Afghan EFL learners’ speaking fluency and accuracy (SFA), learning anxiety (LA), and cognitive load (CL). To do so, 90 Afghan EFL learners were recruited and assigned to two experimental groups (EGs), namely a dynamic assessment group (DAG), a DigAG, and a control group (CG). After that, the three groups were pretested on SFA, LA, and CL. Then, one EG was trained based on the DA, and the other EG was taught based on the DigA, while the CG received common speaking instruction. After finishing the instruction, all groups were given the posttests of SFA, LA, and CL to evaluate the impacts of the treatment on their performances. The findings of the one-way ANOVA test divulged that both EGs outflanked the CG on their posttests. In fact, using dynamic and DigA developed EFL learners’ SFA and CL and reduced their LA. Though both EGs outflanked the CG, the DAG had better improvement than the DigAG on their posttests. At the end of the research, some implications were provided, and some suggestions were recommended for the next studies.

The two main classifications of antidepressant medications are selective norepinephrine reuptake inhibitors (SNRIs) and selective serotonin reuptake inhibitors (SSRIs). Out of the available choices, selective serotonin reuptake inhibitors (SSRIs) have emerged as the most commonly prescribed option. The class demonstrates a greater degree of diversity in its structural characteristics in contrast to its neurochemical effects. Nevertheless, it is important to acknowledge that the chemical composition of a drug within this specific class does not carry substantial significance in the selection process. A comprehensive analysis of the pharmacodynamic and pharmacodynamic properties of antidepressant drugs proves advantageous for clinicians and managed care providers responsible for selecting preferred selective serotonin reuptake inhibitors (SSRIs) from a roster of authorized medications. The physicochemical characteristics, which possess considerable significance, are frequently disregarded except during the drug development stage. Pharmacodynamic properties refer to the physiological and biochemical effects that drugs exert on the human body. It is noteworthy that the inclusion of selective serotonin reuptake inhibitors (SSRIs) and serotonin–norepinephrine reuptake inhibitors (SNRIs) in a comprehensive depression management protocol may demonstrate enhanced effectiveness in clinical environments as opposed to controlled trials.

Objective: In this paper, a proposed technique of motion transmission is introduced, which is based on the crank-rocker principle of motion. The energy transmission action is performed through magnetic force, in which no direct connection is made between the energy source input and the energy load output. Also, to illustrate the concept of motion and to approve the continuity of energy transmission using this proposed technique, a simple model of this mechanism has been built and run, showing the basic sequence of operation. Methodology/analysis: In this mechanical transmission mechanism, one side is rotating and the other side is vibrating, in which any side is energy input (which is usually the vibrating rocker), and the other side is energy output (which is the rotating crank). That seems similar to the classical crank-rocker machine in the four-bar mechanism, but without direct mechanical contact between the input and output energy stream. The concept of motion and mathematical analysis with structuring conditions is provided in this paper, where the dynamic analysis of the system is left for future work. A pilot physical prototype is manufactured and experimentally tested, validating the proposed design. Findings: The structural parameters of this proposed contactless crank-rocker machine have been modelled and simulated using the MATLAB program. It shows that these parameters could be selected and optimized to guarantee the minimum conditions for continued energy transmission. Based on these parameters, a simple model has been built and operated, which illustrates the concept of motion and validates the finding of MATLAB simulation. Novelty/improvement:Contactless crank-rocker motion is a very promising technique. It is possible to apply it in many applications, like the energy harvesting area, and it could be employed certainly in specific designs, such as MEMS, where no other motion transmission types can be used. Doi: 10.28991/ESJ-2022-06-02-07 Full Text: PDF

This paper studies the fidelity level of the extracted LiDAR (Light Detection And Ranging) building point cloud in relation to the original building. In this context, the building point cloud is compared with a reference model. This comparison allows a deep understanding of the point cloud structure with respect to both the actual building and the constructed model. Consequently, the source of the incompatibility in a (reference or constructed) building model is determined and described. Thus, this study considers four aspects of the building point cloud. First, the errors of building point cloud extraction and the undesirable points are quantified. Hence, it is found that the percentages of undesirable points are sometimes considerable (between 15% and 70%). Second, the evaluation of the fit of the altimetry with the reference model shows that the roof plane equations calculated from LiDAR data can be more precise than those of reference model, Third, the segmentability level between different point densities and building typologies are variable. Finally, the per plane comparison mentions the incompatibility of the plane boundaries of point cloud with reference model. Moreover, considerable differences are noted between the theoretical and the true point densities.

Background: Disturbances in the lipid profile and dyslipidemia are prevalent among human immunodeficiency virus (HIV) patients. Furthermore, long-term use of antiretroviral therapy (ART) exacerbates the condition. Data on lipid profile abnormalities among patients receiving highly active antiretroviral treatment in Pakistan are very limited. The present study aims to assess the frequency of dyslipidemia and lipid concentrations among HIV-infected patients receiving active art in comparison to those not receiving any treatment. Methodology: A cross-sectional study was carried out at the HIV clinic of Pakistan Institute of Medical Sciences, Islamabad, from January to July 2018. A total of 100 HIV-infected patients aged 13 to 60 years who gave consent and fulfilled the inclusion criteria were enrolled in the study via non-probability consecutive sampling. All patients who had positive HIV antibodies via the Elisa process were considered positive. Samples were examined for fasting lipids by using standard laboratory procedures. Results: The patients' mean age was 32.7 ± 13.4 ranging between 13-60 years. The total patient population consisted of 71% males and 29% females with a mean BMI of 21.1 ± 3.4 kg/m2. Out of 100, 38% of the study patients had dyslipidemia. The observed prevalence rate of hypertriglyceridemia, hypercholesterolemia, low HDL-C, and high LDL-C were 21%, 30%, 20%, and 16%, respectively. None of the factors were found in significant association with abnormal lipid parameters except the older patients (41-60 years) were at higher risk of hypercholesterolemia [aOR 0.15 (95% CI 0.02-0.92); p=0.04] than the counterpart. Conclusion: The study has demonstrated that a higher percentage of HIV-infected patients have concomitant dyslipidemia with or without art treatment.

Electricity has become an indispensable source of energy, and power lines play a crucial role in the functioning of modern societies. It is essential to inspect power lines promptly and precisely in order to ensure the safe and secure delivery of electricity. In steep and mountainous terrain, traditional surveying methods cannot inspect power lines precisely due to their nature. Remote sensing platforms, such as satellite and aerial images, thermal images, and light detection and ranging (LiDAR) points, were utilised for the detection and inspection of power lines. Nevertheless, with the advancements of remote sensing technologies, in recent years, LiDAR surveying has been favoured for power line corridor (PLC) inspection due to active and weather-independent nature of laser scanning. Laser ranging data and the precise location of the LiDAR can be used to generate a three-dimensional (3D) image of the PLC. The resulting 3D point cloud enables accurate extraction of power lines and measurement of their distances from the forest below. In the literature, there have been many proposals for power line extraction and reconstruction for PLC modelling. This article examines the pros and cons of each domain method, providing researchers involved in three-dimensional modelling of power lines using innovative LiDAR scanning systems with useful guidelines. To achieve these objectives, research papers were analysed, focusing primarily on geoscience-related journals and conferences for the extraction and reconstruction of power lines. There has been a growing interest in examining the extraction and reconstruction of power line spans with single and multi-conductor configurations using different image and point-based techniques. Our study provides a comprehensive overview of the methodologies offered by various approaches using laser scanning data from the perspective of power line extraction applications, as well as to discuss the benefits and drawbacks of each approach. The comparison revealed that, despite the tremendous potential of aerial and mobile laser scanning systems, human intervention and post-processing actions are still required to achieve the desired results. In addition, the majority of the methods have been evaluated on the small datasets, and very few methods have been focused on multi-conductor extraction and reconstruction for power lines modelling. These barriers hinder the automated extraction and reconstruction of power line using LiDAR data and point to unexplored areas for further research and serve as useful guidelines for future research directions. Several promising directions for future LiDAR experiments using deep learning methods are outlined in the hope that they will pave the way for applications of PLC modelling and assessment at a finer scale and on a larger scale.

Accurate building extraction from remotely sensed images is essential for topographic mapping, urban planning, disaster management, navigation, and many other applications [...]

Multisource remote sensing data contain complementary information on land covers, but fusing them is a challenging problem due to the heterogeneous nature of the data. This article aims to extract and integrate information from hyperspectral image (HSI) and light detection and ranging (LiDAR) data for land cover classification. As there is a scarcity of a large number of training samples for remotely sensed hyperspectral and LiDAR data, in this article, we propose a model, which is able to perform impressively using a limited number of training samples by extracting effective features representing different characteristics of objects of interest from these two complementary data sources (HSI and LiDAR). A novel feature extraction method named inverse coefficient of variation (ICV) is introduced for HSI, which considers the Gaussian probability of neighborhood between every pair of bands. We, then, propose a two-stream feature fusion approach to integrate the ICV feature with several features extracted from HSI and LiDAR data. We incorporate a fusion unit named canonical correlation analysis as a basic unit for fusing two different sets of features within each stream. We also incorporate the concept of ensemble classification where the features produced by two-stream fusion are distributed into subsets and transformed to improve the feature quality. We compare our method with the existing state-of-the-art methods, which are based on deep learning or handcrafted feature extraction or using both of them. Experimental results show that our proposed approach performs better than other existing methods with a limited number of training samples.

Various methods for automatic building extraction from remote sensing data including light detection and ranging (LiDAR) data have been proposed over the last two decades but a standard metric for evaluation of the extracted building boundary has not been found yet. An extracted building boundary from LiDAR data usually has a zigzag pattern with missing detail, which makes it hard to compare the boundary with its reference. The existing metrics do not consider the significant point (e.g., corner) correspondences, therefore, cannot identify individual extralap and underlap areas in the extracted boundary. This article proposes an evaluation metric for the extracted boundary based on a newly proposed robust corner correspondence algorithm that finds one-to-one true corner correspondences between the reference and extracted boundaries. Assuming a building has a rectilinear shape, corners and lines are first detected for the extracted boundary. Then, corner correspondences are obtained between the extracted and reference boundaries. Each corner has two corresponding lines on its two sides that ideally are perpendicular to each other. The corner correspondences are finally ranked based on their distance, angle, and parallelism of corresponding lines. The metric is defined as the average minimum distance davg from the extracted boundary points to their corresponding reference lines. Extralap and underlap areas are identified by comparing the point distances with davg. In experiments, the proposed metric performs more realistic than the existing metrics and finds the individual extralap and underlap areas effectively.

The complexity of achieving optimal power flow in the presence of renewable resources decreases the accuracy and optimality levels of the power system due to the associated intermittency and uncertainty. The increased challenge of large-scale deployment of wind energy necessitates the proper modeling of wind impact on power system security and reliability levels. This article discusses a new reliable power flow optimization tool that accounts for wind power availability and uncertainty. An accurate wind forecast model is created to maintain power system security considering wind power variability. The error of the forecasting phase is included in the proposed model to accurately predict the available wind power. In this work, the scattered wind data is converted into informative frequency distribution considering the effect of averaging around integers, halves, and quarters. The proposed method maximizes the utilization level of wind energy without deteriorating the system security. The accuracy of the new proposed work is presented by comparing its results with other models discussed in the literature. A complete and integrated formulation of the objective function has been accomplished. The cost function includes transmission losses, generation operating costs, generation gas emissions, and valve-point effects. Reliable and efficient optimization algorithms are adopted to minimize the established cost function of the system-namely, teaching-learning-based optimization and symbiotic organisms search algorithms. The effectiveness of the proposed approach is validated using the IEEE 39-bus system.

Paracetamol and nonsteroidal anti-inflammatory drugs are widely used in the management of respiratory viral infections. This study aimed to determine the effects of the most commonly used analgesics (paracetamol, ibuprofen, and diclofenac) on the mRNA expression of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) entry and arachidonic-acid-metabolizing genes in mouse lungs. A total of twenty eight Balb/c mice were divided into four groups and treated separately with vehicle, paracetamol, ibuprofen, and diclofenac in clinically equivalent doses for 14 days. Then, the expressions of SARS-CoV-2 entry, ACE2, TMPRSS2, and Ctsl genes, in addition to the arachidonic-acid-metabolizing cyp450, cox, and alox genes, were analyzed using real-time PCR. Paracetamol increased the expressions of TMPRSS2 and Ctsl genes by 8.5 and 5.6 folds, respectively, while ibuprofen and diclofenac significantly decreased the expression of the ACE2 gene by more than 2.5 folds. In addition, all tested drugs downregulated (p < 0.05) cox2 gene expression, and paracetamol reduced the mRNA levels of cyp4a12 and 2j5. These molecular alterations in diclofenac and ibuprofen were associated with pathohistological alterations, where both analgesics induced the infiltration of inflammatory cells and airway wall thickening. It is concluded that analgesics such as paracetamol, ibuprofen, and diclofenac alter the expression of SARS-CoV-2 entry and arachidonic-acid-metabolizing genes in mouse lungs.

BackgroundThere is a growing awareness that addressing chronic as well as acute health conditions may contribute importantly to the well-being of displaced populations, but eye care service has generally not been prioritized in crisis situations. We describe a replicable model of eye care provision as delivered by Orbis International and local partners to the Rohingya and host population in Cox's Bazar, Bangladesh, and characterize the burden of vision impairment and demand for sight-restoring services in this setting.Methods and findingsOrbis International and local secondary facility Cox's Bazar Baitush Sharaf Hospital (CBBSH) provide eye care support to the Rohingya population and the host community of all ages in Cox's Bazar, Bangladesh, with fixed vision screening locations established in Camps 4 and 11 of the Kutupalong refugee settlement. Structured outreach targets these camps and four surrounding local subdistricts, with referrals made as needed for refraction (glasses measurement) and cataract surgery to CBBSH. Between February 2018 and March 2019, 48,105 displaced Rohingya (70.3%, among whom 71.6% were children and 46.5% women) and 20,357 local residents (29.7%, 88.5% children, 54.4% women) underwent vision screening. Displaced Rohingya sought services from a total of 12 surrounding camps, within which coverage was 17.3%, including 43.3% (27,027/62,424) of children aged 5-11 years and 60.0% (5,315/8,857) of adults ≥ 60 years old. The prevalence of blindness (presenting acuity < 3/60) among Rohingya patients exceeded that among local residents by 3- to 6-fold in each 10-year age group between 18 and 59 years (P < 0.001 comparing vision between the two groups in this age range), and the prevalence of cataract requiring surgery was also higher in Rohingya patients (18-29 years: 4.67% versus 1.80%, P = 0.0019; 30-39: 7.61% versus 2.39%, P < 0.001; and 40-49 years: 7.91% versus 3.77%, P = 0.0014). A limitation of the study is lack of data on population prevalence of eye disease.ConclusionsThe burden of untreated eye disease is very high among the Rohingya, particularly those in their peak working years who could contribute most to the resiliency of their community. Demand for eye care service is also great among children and adults in this population with many competing healthcare priorities. Research is needed, building on strong evidence of benefit in settled populations, to explore the specific impact of vision care on the well-being of displaced populations.

This paper suggests a new algorithm for automatic building point cloud filtering based on the Z coordinate histogram. This operation aims to select the roof class points from the building point cloud, and the suggested algorithm considers the general case where high trees are associated with the building roof. The Z coordinate histogram is analyzed in order to divide the building point cloud into three zones: the surrounding terrain and low vegetation, the facades, and the tree crowns and/or the roof points. This operation allows the elimination of the first two classes which represent an obstacle toward distinguishing between the roof and the tree points. The analysis of the normal vectors, in addition to the change of curvature factor of the roof class leads to recognizing the high tree crown points. The suggested approach was tested on five datasets with different point densities and urban typology. Regarding the results’ accuracy quantification, the average values of the correctness, the completeness, and the quality indices are used. Their values are, respectively, equal to 97.9%, 97.6%, and 95.6%. These results confirm the high efficacy of the suggested approach.

Roof plane segmentation is a complex task since point cloud data carry no connection information and do not provide any semantic characteristics of the underlying scanned surfaces. Point cloud density, complex roof profiles, and occlusion add another layer of complexity which often encounter in practice. In this article, we present a new technique that provides a better interpolation of roof regions where multiple surfaces intersect creating non-manifold points. As a result, these geometric features are preserved to achieve automated identification and segmentation of the roof planes from unstructured laser data. The proposed technique has been tested using the International Society for Photogrammetry and Remote Sensing benchmark and three Australian datasets, which differ in terrain, point density, building sizes, and vegetation. The qualitative and quantitative results show the robustness of the methodology and indicate that the proposed technique can eliminate vegetation and extract buildings as well as their non-occluding parts from the complex scenes at a high success rate for building detection (between 83.9% and 100% per-object completeness) and roof plane extraction (between 73.9% and 96% per-object completeness). The proposed method works more robustly than some existing methods in the presence of occlusion and low point sampling as indicated by the correctness of above 95% for all the datasets.

Lithium imposes several cellular effects allegedly through multiple physiological mechanisms. Membrane depolarization is a potential unifying concept of these mechanisms. Multiple inherent imperfections of classical electrophysiology limit its ability to fully explain the depolarizing effect of lithium ions; these include incapacity to explain the high resting permeability of lithium ions, the degree of depolarization with extracellular lithium concentration, depolarization at low therapeutic concentration, or the differences between the two lithium isotopes Li-6 and Li-7 in terms of depolarization. In this study, we implemented a mathematical model that explains the quantum tunneling of lithium ions through the closed gates of voltage-gated sodium channels as a conclusive approach that decodes the depolarizing action of lithium. Additionally, we compared our model to the classical model available and reported the differences. Our results showed that lithium can achieve high quantum membrane conductance at the resting state, which leads to significant depolarization. The quantum model infers that quantum membrane conductance of lithium ions emerges from quantum tunneling of lithium through the closed gates of sodium channels. It also differentiates between the two lithium isotopes (Li-6 and Li-7) in terms of depolarization compared with the previous classical model. Moreover, our study listed many examples of the cellular effects of lithium and membrane depolarization to show similarity and consistency with model predictions. In conclusion, the study suggests that lithium mediates its multiple cellular effects through membrane depolarization, and this can be comprehensively explained by the quantum tunneling model of lithium ions.

AimTo determine the prevalence and causes of blindness, vision impairment and cataract surgery coverage among Rohingya refugees aged ≥ 50 years residing in camps in Cox's Bazar, Bangladesh.MethodsWe used the Rapid Assessment of Avoidable Blindness (RAAB) methodology to select 76 clusters of 50 participants aged ≥ 50 years with probability proportionate to size. Demographic and cataract surgery data were collected using questionnaires, visual acuity was assessed per World Health Organization criteria and examinations were conducted by torch, and with direct ophthalmoscopy in eyes with pinhole-corrected vision ResultsWe examined 3,629 of 3800 selected persons (95.5%). Age and sex adjusted prevalence of blindness (3/60 to ≤6/60), moderate visual impairment (MVI; >6/60 to ≤6/18), and early visual impairment (EVI; >6/18 to ≤6/12) were 2.14%, 2.35%, 9.68% and 14.7% respectively. Cataract was responsible for 75.0% of blindness and 75.8% of SVI, while refractive error caused 47.9% and 90.9% of MVI and EVI respectively. Most vision loss (95.9%) was avoidable. Cataract surgical coverage among the blind was 81.2%. Refractive error was detected in 17.1% (n = 622) of participants and 95.2% (n = 592) of these did not have spectacles. In the full Rohingya cohort of 76,692, approximately 10,000 surgeries are needed to correct all eyes impaired (ConclusionThe prevalence of blindness was lower than expected for a displaced population, in part due to few Rohingya being ≥60 years and the camp's good access to cataract surgery. We suggest the United Nations High Commissioner for Refugees include eye care among recommended health services for all refugees with long-term displacement.

Acute pancreatitis (AP) is a severe and potentially fatal disease caused predominantly by alcohol excess and gallstones, which lacks a specific therapy. The role of Receptor-Interacting Protein Kinase 1 (RIPK1), a key component of programmed necrosis (Necroptosis), is unclear in AP. We assessed the effects of RIPK1 inhibitor Necrostatin-1 (Nec-1) and RIPK1 modification (RIPK1K45A: kinase dead) in bile acid (TLCS-AP), alcoholic (FAEE-AP) and caerulein hyperstimulation (CER-AP) mouse models. Involvement of collateral Nec-1 target indoleamine 2,3-dioxygenase (IDO) was probed with the inhibitor Epacadostat (EPA). Effects of Nec-1 and RIPK1K45A were also compared on pancreatic acinar cell (PAC) fate in vitro and underlying mechanisms explored. Nec-1 markedly ameliorated histological and biochemical changes in all models. However, these were only partially reduced or unchanged in RIPK1K45A mice. Inhibition of IDO with EPA was protective in TLCS-AP. Both Nec-1 and RIPK1K45A modification inhibited TLCS- and FAEE-induced PAC necrosis in vitro. Nec-1 did not affect TLCS-induced Ca2+ entry in PACs, however, it inhibited an associated ROS elevation. The results demonstrate protective actions of Nec-1 in multiple models. However, RIPK1-dependent necroptosis only partially contributed to beneficial effects, and actions on targets such as IDO are likely to be important.

Existing approaches that extract buildings from point cloud data do not select the appropriate neighbourhood for estimation of normals on individual points. However, the success of these approaches depends on correct estimation of the normal vector. In most cases, a fixed neighbourhood is selected without considering the geometric structure of the object and the distribution of the input point cloud. Thus, considering the object structure and the heterogeneous distribution of the point cloud, this paper proposes a new effective approach for selecting a minimal neighbourhood, which can vary for each input point. For each point, a minimal number of neighbouring points are iteratively selected. At each iteration, based on the calculated standard deviation from a fitted 3D line to the selected points, a decision is made adaptively about the neighbourhood. The selected minimal neighbouring points make the calculation of the normal vector accurate. The direction of the normal vector is then used to calculate the inside fold feature points. In addition, the Euclidean distance from a point to the calculated mean of its neighbouring points is used to make a decision about the boundary point. In the context of the accuracy evaluation, the experimental results confirm the competitive performance of the proposed approach of neighbourhood selection over the state-of-the-art methods. Based on our generated ground truth data, the proposed fold and boundary point extraction techniques show more than 90% F1-scores.

Overhead high-voltage conductors are the chief components of power lines and their safety has a strong influence on social and daily life. In the recent decade, the airborne laser scanning (ALS) technique has been widely used to capture the three-dimensional (3D) information of power lines and surrounding objects. Most of the existing methods focused on extraction of single conductors or extracted all conductors as one object class by applying machine learning techniques. Nevertheless, power line corridors (PLCs) are built with multi-loop, multi-phase structures (bundle conductors) and exist in intricate environments (e.g., mountains and forests), and thus raise challenges to process ALS data for extraction of individual conductors. This paper proposes an automated method to extract individual subconductors in bundles from complex structure of PLCs using a combined image- and point-based approach. First, the input point cloud data are grouped into 3D voxel grid and PL points and separated from pylon and tree points using the fact that pylons and trees are vertical objects while power lines are non-vertical objects. These pylons are further separated from trees by employing a statistical analysis technique and used to extract span points between two consecutive pylons; then, by using the distribution properties of power lines in each individual span, the bundles located at different height levels are extracted using image-based processing; finally, subconductors in each bundle are detected and extracted by introducing a window that slides over the individual bundle. The orthogonal plane transformation and recursive clustering procedures are exploited in each window position and a point-based processing is conducted iteratively for extraction of complete individual subconductors in each bundle. The feasibility and validity of the proposed method are verified on two Australian sites having bundle conductors in high-voltage transmission lines. Our experiments show that the proposed method achieves a reliable result by extracting the real structure of bundle conductors in power lines with correctness of 100% and 90% in the two test sites, respectively.

We investigated the effect of reduced graphene oxide (rGO) doping on the birefringence of 5CB liquid crystal (LC). The characteristics of the synthesized rGO and LC-rGO composite with different rGO concentrations were analyzed by atomic force microscopy, X-ray photoelectron spectroscopy, white light polarized microscopy, voltage-dependent transmission measurement, and differential scanning calorimetry. We found that doping LC with an appropriate concentration of rGO enhances the birefringence of the LC. This is mainly due to the improved anisotropy of polarizability, which stems from the high shape anisotropy of rGO. However, the aggregation of rGO reduces the birefringence by decreasing the anisotropy of polarizability as well as the order parameter. Our study shows the promising potential of LC-rGO for developing various electro-optic devices that offer improved electro-optic effects.

Periodic building change detection is important for many applications, including disaster management. Building map databases need to be updated based on detected changes so as to ensure their currency and usefulness. This paper first presents a graphical user interface (GUI) developed to support the creation of a building database from building footprints automatically extracted from LiDAR (light detection and ranging) point cloud data. An automatic building change detection technique by which buildings are automatically extracted from newly-available LiDAR point cloud data and compared to those within an existing building database is then presented. Buildings identified as totally new or demolished are directly added to the change detection output. However, for part-building demolition or extension, a connected component analysis algorithm is applied, and for each connected building component, the area, width and height are estimated in order to ascertain if it can be considered as a demolished or new building-part. Using the developed GUI, a user can quickly examine each suggested change and indicate his/her decision to update the database, with a minimum number of mouse clicks. In experimental tests, the proposed change detection technique was found to produce almost no omission errors, and when compared to the number of reference building corners, it reduced the human interaction to 14% for initial building map generation and to 3% for map updating. Thus, the proposed approach can be exploited for enhanced automated building information updating within a topographic database.

Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications [...]

Automatic extraction of building roofs from remote sensing data is important for many applications, including 3D city modeling. This paper proposes a new method for automatic segmentation of raw LIDAR (light detection and ranging) data. Using the ground height from a DEM (digital elevation model), the raw LIDAR points are separated into two groups. The first group contains the ground points that form a “building mask”. The second group contains non-ground points that are clustered using the building mask. A cluster of points usually represents an individual building or tree. During segmentation, the planar roof segments are extracted from each cluster of points and refined using rules, such as the coplanarity of points and their locality. Planes on trees are removed using information, such as area and point height difference. Experimental results on nine areas of six different data sets show that the proposed method can successfully remove vegetation and, so, offers a high success rate for building detection (about 90% correctness and completeness) and roof plane extraction (about 80% correctness and completeness), when LIDAR point density is as low as four points/m2. Thus, the proposed method can be exploited in various applications.

High density airborne point cloud data have become an important means for modelling and maintenance of power line corridors (PLCs). As the amount of data in a dense point cloud is large, even in a small area, automatic detection of pylon locations can offer a significant advantage by reducing the number of points that need to be processed in subsequent steps, i.e., the extraction of individual pylons and wires. However, the existing solutions mostly overlook this advantage by processing all of the available data at one time, which hinders their application to large datasets. Moreover, the presence of high vegetation and hilly terrain may challenge many of the existing methods, since vertically overlapping objects (e.g., trees and wires) may not be effectively segmented using a single height threshold. For extraction of pylons and wires, this paper proposes a novel approach which involves converting the input points at different height levels into binary masks. Long straight lines are extracted from these masks and convex hulls around the lines at individual height levels are used to form series of hulls across the height levels. The series of hulls are then projected onto a horizontal plane to form individual corridors. A number of height gaps, where there are no objects between the vegetation and the bottom-most wire, are then estimated. The height gaps along with the height levels consider the presence of hilly terrain as well as high vegetation within the PLCs. By using only the non-ground points within the extracted corridors and height gaps, the pylons are detected. The estimated height gaps are further exploited to define robust seed regions for the detected pylons. The seed regions thereafter are grown to extract the complete pylons. Finally, only the points between the locations of two successive pylons are used to extract points of individual wires. It first counts the number of wires within a power line span and, then, iteratively obtains individual wire points. When tested on two large Australian datasets, the proposed approach exhibited high object-based performance (correctness for pylons and wires of 100% and 99.6%, respectively) and high point-based performance (completeness for pylons and wires of 98.1% and 95%, respectively). Moreover, the planimetric accuracy for the detected pylons was 0.10 m. Thus, the proposed approach is demonstrated to be useful in effective extraction and modelling of pylons and wires.

Low linear density polyethylene/organo-montmorillonite (LLDPE/OMMT) nanocomposites at 1–5 wt% OMMT loading were prepared by the melt intercalation technique. The OMMT was synthesized via an ion exchange reaction by replacing the interlayer of sodium ions (Na+ ) in the repeating unit of silicate layers of montmorillonite (MMT) with the cationic surfactant in the form of trihexyltetradecylphosphonium (THTDP) ions. The obtained OMMT and its nanocomposites were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, and elemental and thermogravimetric analyses. The interlayer spacing of MMT expanded from 1.41 to 2.29 nm due to the accommodation of THTDP ions in the intergallery of OMMT. The introduction of THTDP in the interlayer of OMMT rendered better dispersion of OMMT layers in the LLDPE/OMMT nanocomposites and significantly improved the thermal degradation properties of nanocomposites as compared to the pristine LLDPE.

Three-dimensional (3-D) reconstruction of building roofs can be an essential prerequisite for 3-D building change detection, which is important for detection of informal buildings or extensions and for update of 3-D map database. However, automatic 3-D roof reconstruction from the remote sensing data is still in its development stage for a number of reasons. For instance, there are difficulties in determining the neighbourhood relationships among the planes on a complex building roof, locating the step edges from point cloud data often requires additional information or may impose constraints, and missing roof planes attract human interaction and often produces high reconstruction errors. This research introduces a new 3-D roof reconstruction technique that constructs an adjacency matrix to define the topological relationships among the roof planes. It identifies any missing planes through an analysis using the 3-D plane intersection lines between adjacent planes. Then, it generates new planes to fill gaps of missing planes. Finally, it obtains complete building models through insertion of approximate wall planes and building floor. The reported research in this paper then uses the generated building models to detect 3-D changes in buildings. Plane connections between neighbouring planes are first defined to establish relationships between neighbouring planes. Then, each building in the reference and test model sets is represented using a graph data structure. Finally, the height intensity images, and if required the graph representations, of the reference and test models are directly compared to find and categorise 3-D changes into five groups: new, unchanged, demolished, modified and partially-modified planes. Experimental results on two Australian datasets show high object- and pixel-based accuracy in terms of completeness, correctness, and quality for both 3-D roof reconstruction and change detection techniques. The proposed change detection technique is robust to various changes including addition of a new veranda to or removal of an existing veranda from a building and increase of the height of a building.

The Fms-like tyrosine kinase-3 (FLT3) is a receptor tyrosine kinase that plays a key role in cell survival, proliferation, and differentiation of hematopoietic stem cells. Mutations of FLT3 were first described in 1997 and account for the most frequent molecular mutations in acute myeloid leukemia (AML). AML patients with FLT3 internal tandem duplication (ITD) mutations have poor cure rates the prognostic significance of point mutations; tyrosine kinase domain (TKD) is still unclear. We analyzed the frequency of FLT3 mutations (ITD and D835) in patients with AML at diagnosis; no sufficient data currently exist regarding FLT3 mutations in Saudi AML patients. This study was aimed at evaluating the frequency of FLT3 mutations in patients with AML and its significance for prognosis. The frequency of FLT3 mutations in our study (18.56%) was lower than many of the reported studies, FLT3-ITD mutations were observed in 14.4%, and FLT3-TKD in 4.1%, of 97 newly diagnosed AML patients (82 adult and 15 pediatric). Our data show significant increase of FLT3 mutations in male more than female (13 male, 5 female). Our results support the view that FLT3-ITD mutation has strong prognostic factor in AML patients and is associated with high rate of relapse, and high leucocytes and blast count at diagnosis and relapse.

Existing automatic building extraction methods are not effective in extracting buildings which are small in size and have transparent roofs. The application of large area threshold prohibits detection of small buildings and the use of ground points in generating the building mask prevents detection of transparent buildings. In addition, the existing methods use numerous parameters to extract buildings in complex environments, e.g., hilly area and high vegetation. However, the empirical tuning of large number of parameters reduces the robustness of building extraction methods. This paper proposes a novel Gradient-based Building Extraction (GBE) method to address these limitations. The proposed method transforms the Light Detection And Ranging (LiDAR) height information into intensity image without interpolation of point heights and then analyses the gradient information in the image. Generally, building roof planes have a constant height change along the slope of a roof plane whereas trees have a random height change. With such an analysis, buildings of a greater range of sizes with a transparent or opaque roof can be extracted. In addition, a local colour matching approach is introduced as a post-processing stage to eliminate trees. This stage of our proposed method does not require any manual setting and all parameters are set automatically from the data. The other post processing stages including variance, point density and shadow elimination are also applied to verify the extracted buildings, where comparatively fewer empirically set parameters are used. The performance of the proposed GBE method is evaluated on two benchmark data sets by using the object and pixel based metrics (completeness, correctness and quality). Our experimental results show the effectiveness of the proposed method in eliminating trees, extracting buildings of all sizes, and extracting buildings with and without transparent roof. When compared with current state-of-the-art building extraction methods, the proposed method outperforms the existing methods in various evaluation metrics.

The development of robust and accurate methods for automatic building detection and regularisation using multisource data continues to be a challenge due to point cloud sparsity, high spectral variability, urban objects differences, surrounding complexity, and data misalignment. To address these challenges, constraints on object’s size, height, area, and orientation are generally benefited which adversely affect the detection performance. Often the buildings either small in size, under shadows or partly occluded are ousted during elimination of superfluous objects. To overcome the limitations, a methodology is developed to extract and regularise the buildings using features from point cloud and orthoimagery. The building delineation process is carried out by identifying the candidate building regions and segmenting them into grids. Vegetation elimination, building detection and extraction of their partially occluded parts are achieved by synthesising the point cloud and image data. Finally, the detected buildings are regularised by exploiting the image lines in the building regularisation process. Detection and regularisation processes have been evaluated using the ISPRS benchmark and four Australian data sets which differ in point density (1 to 29 points/m2), building sizes, shadows, terrain, and vegetation. Results indicate that there is 83% to 93% per-area completeness with the correctness of above 95%, demonstrating the robustness of the approach. The absence of over- and many-to-many segmentation errors in the ISPRS data set indicate that the technique has higher per-object accuracy. While compared with six existing similar methods, the proposed detection and regularisation approach performs significantly better on more complex data sets (Australian) in contrast to the ISPRS benchmark, where it does better or equal to the counterparts.