Validation Data Research Articles

Fiqh education for adolescent girls: Study of the latanun nisa in temanggung, indonesia

Many adolescent girls in Brongkol Village lack understanding regarding the distinction between menstrual bleeding (haid) and abnormal vaginal bleeding (istihadhah), crucial aspects of the fiqh of worship governing Muslim women's daily lives. This study aims to explore the urgency, implications, advantages, and disadvantages of women's fiqh education through the Ianatun Nisa book among adolescent girls in Brongkol Village, Temanggung Sub-District. Conducted as field research, this qualitative study adopts a phenomenological approach, gathering data through interviews, observations, and documentation. Data analysis involves collection, reduction, presentation, and conclusion drawing, with data validity ensured through source triangulation. Findings indicate the significance of women's fiqh education for adolescent girls in Brongkol Village. Studying menstrual bleeding (haid) and abnormal vaginal bleeding (istihadhah) is obligatory, impacting their daily worship practices like prayer and fasting. Positive impacts include improved understanding through expository and reflective approaches, facilitated by lecture and discussion methods. However, challenges such as limited Arabic pegon Javanese script literacy among adolescent girls and methodological constraints hinder effective implementation.

Validity and reliability of Persian version of Low Physical Activity Questionnaire (LoPAQ).

The Low Physical Activity Questionnaire (LoPAQ) was specifically developed to measure the low activity level observed in extremely inactive hemodialysis (HD) patients. This study aims to evaluate reliability and validity of Persian version of the LoPAQ. This study was a cross sectional study, conducted in three HD centers in Iran. The LoPAQ was translated into Persian. After cultural adaptions, it was filled out by 120 HD patiens. Convergent validity, was evaluated by calculating the correlations among the Persian version of the LoPAQ and Persian version of the Community Healthy Adults Model Program for Seniors (CHAMPS) questionnaire, physical function scale of the SF-36 and physical function (Short Physical Performance Battery (SPPB) test) using Spearman's correlation coefficients. The test-retest reliability was analyzed using the intraclass correlation coefficient (ICC). In total, 109 patients completed all of the questionnaires, took part in physical performance tests and had valid data. Their mean age was 64 ± 11 years, with a dialysis history of 31 ± 10 months. For total calories, there was a strong correlation between the Persian version of the LoPAQ and CHAMPS-measured physical activity (rho = 0.85, p < 0.001). In addition, the higher physical activity level reported by Persian version of the LoPAQ was also correlated with better self-reported physical function (rho = 0.7, p < 0.001) and better physical performance (rho = 0.67, p < 0.001). The ICC ranged from 0.65 to 0.78, indicating strong reliability. The assessment of the validity and reliability of the Persian version of the questionnaire confirmed its suitability for evaluating the level of physical activity. ClinicalTrials.gov Identifier: NCT05930964, Registered on 05/07/2023. Registered trial name: Validity and Reliability of Persian Version of Low Physical Activity Questionnaire (LoPAQ).

Innovation and Strategy of Madrasah Ibtidaiyah Teachers in Implementing the Independent Curriculum

The aim of the research is to describe teachers' readiness to implement the independent curriculum at the MI Muhammadiyah Kartasura Special Program. This research is descriptive and qualitative. The data collection techniques used were observation, interviews, and documentation. Data validity using source triangulation techniques. And the data analysis used is an interactive analysis model with stages of data collection, data reduction, data presentation, and drawing conclusions. The research results show that teachers are ready to implement the independent curriculum. There are five aspects that have been carried out by teachers before implementing the independent curriculum in the learning process, namely readiness in understanding the curriculum structure, readiness to plan learning, learning process readiness, teaching module readiness, and learning assessment readiness.

A comprehensive study on changes in coastal hydrodynamics associated with cyclonic activity

A Mediterranean cyclone is a weather phenomenon capable of producing extremely severe conditions, including heavy rainfall and strong winds. Between March 24 and 26, 2023, a cyclone passed along the western Egyptian Mediterranean coast, spanning three days. This paper aims to investigate the cyclone's impact on wave characteristics, focusing particularly on simulating changes in the energy transported from wind to waves during its passage, which constitutes the core objective of this study. The research methodology involved collecting meteorological and hydrodynamic data over five days from March 23 to 27, 2023, utilizing databases of the Bologna Limited Area Model (BOLAM) and the General Bathymetric Chart of the Oceans (GEBCO). This data, combined with field data for model calibration and validation, was analyzed using the Simulating the WAves Nearshore (SWAN) model packaged within the Delft 3D hydrodynamical model, integrated with other data manipulation tools. (SWAN) demonstrated the ability to simulate energy transport during extreme weather events along the coastal area with high resolution, up to 500 m. The results indicate a significant increase in significant wave height, reaching up to 2.5 m, and disturbances in wind direction, with velocities exceeding 10 m per second. These conditions pose risks to the infrastructure in some cities along the study area and have severe impacts on coastal communities. A notable finding from the simulations is the excess energy transport, which reached up to 12,000 watts per meter over the sea surface during the cyclone. Furthermore, calibration and validation results affirm the (SWAN) model's capability to accurately study wave characteristics.

Improved brain metastases segmentation using generative adversarial network and conditional random field optimization mask R-CNN.

In radiotherapy, the delineation of the gross tumor volume (GTV) in brain metastases using computed tomography (CT) simulation localization is very important. However, despite the criticality of this process, a pronounced gap exists in the availability of tools tailored for the automatic segmentation of the GTV based on CT simulation localization images. This study aims to fill this gap by devising an effective tool specifically for the automatic segmentation of the GTV using CT simulation localization images. A dual-network generative adversarial network (GAN) architecture was developed, wherein the generator focused on refining CT images for more precise delineation, and the discriminator differentiated between real and augmented images. This architecture was coupled with the Mask R-CNN model to achieve meticulous GTV segmentation. An end-to-end training process facilitated the integration between the GAN and Mask R-CNN functionalities. Furthermore, a conditional random field (CRF) was incorporated to refine the initial masks generated by the Mask R-CNN model to ensure optimal segmentation accuracy. The performance was assessed using key metrics, namely, the Dice coefficient (DSC), intersection over union (IoU), accuracy, specificity, and sensitivity. The GAN+Mask R-CNN+CRF integration method in this study performs well in GTV segmentation. In particular, the model has an overall average DSC of 0.819±0.102 and an IoU of 0.712±0.111 in the internal validation. The overall average DSC in the external validation data is 0.726±0.128 and the IoU is 0.640±0.136. It demonstrates favorable generalization ability. The integration of the GAN, Mask R-CNN, and CRF optimization provides a pioneering tool for the sophisticated segmentation of the GTV in brain metastases using CT simulation localization images. The method proposed in this study can provide a robust automatic segmentation approach for brain metastases in the absence of MRI.

Analysis of Bovine Lactoferrin in Infant Formula and Adult Nutritional Products by Optical Biosensor Immunoassay: Collaborative Study, Final Action AOAC 2021.07.

Bovine lactoferrin is increasingly being used as an ingredient in infant formula manufacture to enhance nutritional efficacy through the provision of growth, immunoprotective and antimicrobial factors to the neonate. To evaluate method reproducibility of AOAC 2021.07 Official First Action method for compliance with the performance requirements described in Standard Method Performance Requirement (SMPR®) 2020.005. Eight laboratories participated in the analysis of blind-duplicate samples of seven nutritional products. Samples were diluted in buffer, and an optical biosensor immunoassay was used in a direct assay format to quantitate bovine lactoferrin by its interaction with an immobilized anti-lactoferrin antibody. Quantitation was accomplished by the external standard technique with interpolation from a 4-parameter calibration regression. After outliers were removed, precision as reproducibility was found to be within limits set in SMPR 2020.005 (≤ 9%) for six out of seven samples and all had acceptable HorRatR values ranging from 1.0 to 2.1. Additionally, comparison with an alternative independent Stakeholder Panel on Infant Formula and Adult Nutritionals (SPIFAN) First Action method (heparin clean-up LC UV), showed negligible difference between results. The method described is suitable for the quantification of intact, undenatured bovine lactoferrin in powdered infant formulas. The SPIFAN Expert Review Panel evaluated the method and accompanying validation data from this multi-laboratory testing study in July 2023 and recommended Official Method 2021.07 for adoption as a Final Action Official Method. A multi-laboratory validation study of an automated optical biosensor immunoassay for the determination of intact, undenatured bovine lactoferrin is described.

Validating the accuracy of Omron HEM-7372T1-AZAZ (BP5460) in monitoring blood pressure according to the ISO 81060-2:2018+Amd 1:2020 protocol in the general population.

The performance of the Omron HEM-7372T1-AZAZ (BP5460) in monitoring blood pressure (BP) in the upper arm was validated in accordance with the International Organization for Standardization (ISO) 81060-2:2018 (E) and amendment (Amd)1:2020 protocol. The device was used to assess 98 participants who fulfilled the inclusion criteria, including the range of arm circumference and systolic and diastolic BP, in accordance with the protocol. Data validation and analysis were performed according to the manufacturer's instructions. In the ISO validation procedure (criterion 1), the mean ± SD of the differences between test device readings and reference BP was 0.3 ± 6.17/3.6 ± 5.26 mmHg (systolic/diastolic). These data fulfilled the ISO requirements of ≤5±≤8 mmHg. The mean differences between the observer measurements and Omron HEM-7372T1-AZAZ (BP5460) readings were 0.3 ± 4.82 mmHg for systolic BP and 3.6 ± 4.78 mmHg for diastolic BP, fulfilling criterion 2 with SD values of ≤6.95 for SBP and ≤5.89 for DBP. Therefore, two ISO criteria were fulfilled. The Omron HEM-7372T1-AZAZ BP monitor fulfilled the requirements of the ISO validation standard. This device can be recommended for home BP measurements in the general population.

Deformable registration of magnetic resonance images using unsupervised deep learning in neuro-/radiation oncology

PurposeAccurate deformable registration of magnetic resonance imaging (MRI) scans containing pathologies is challenging due to changes in tissue appearance. In this paper, we developed a novel automated three-dimensional (3D) convolutional U-Net based deformable image registration (ConvUNet-DIR) method using unsupervised learning to establish correspondence between baseline pre-operative and follow-up MRI scans of patients with brain glioma.MethodsThis study involved multi-parametric brain MRI scans (T1, T1-contrast enhanced, T2, FLAIR) acquired at pre-operative and follow-up time for 160 patients diagnosed with glioma, representing the BraTS-Reg 2022 challenge dataset. ConvUNet-DIR, a deep learning-based deformable registration workflow using 3D U-Net style architecture as a core, was developed to establish correspondence between the MRI scans. The workflow consists of three components: (1) the U-Net learns features from pairs of MRI scans and estimates a mapping between them, (2) the grid generator computes the sampling grid based on the derived transformation parameters, and (3) the spatial transformation layer generates a warped image by applying the sampling operation using interpolation. A similarity measure was used as a loss function for the network with a regularization parameter limiting the deformation. The model was trained via unsupervised learning using pairs of MRI scans on a training data set (n = 102) and validated on a validation data set (n = 26) to assess its generalizability. Its performance was evaluated on a test set (n = 32) by computing the Dice score and structural similarity index (SSIM) quantitative metrics. The model’s performance also was compared with the baseline state-of-the-art VoxelMorph (VM1 and VM2) learning-based algorithms.ResultsThe ConvUNet-DIR model showed promising competency in performing accurate 3D deformable registration. It achieved a mean Dice score of 0.975 ± 0.003 and SSIM of 0.908 ± 0.011 on the test set (n = 32). Experimental results also demonstrated that ConvUNet-DIR outperformed the VoxelMorph algorithms concerning Dice (VM1: 0.969 ± 0.006 and VM2: 0.957 ± 0.008) and SSIM (VM1: 0.893 ± 0.012 and VM2: 0.857 ± 0.017) metrics. The time required to perform a registration for a pair of MRI scans is about 1 s on the CPU.ConclusionsThe developed deep learning-based model can perform an end-to-end deformable registration of a pair of 3D MRI scans for glioma patients without human intervention. The model could provide accurate, efficient, and robust deformable registration without needing pre-alignment and labeling. It outperformed the state-of-the-art VoxelMorph learning-based deformable registration algorithms and other supervised/unsupervised deep learning-based methods reported in the literature.

Boundary Condition Options for Carotid Bifurcation Analysis, Using Doppler Velocity Measurements

The Common Carotid Artery plays a vital role in supplying the brain, and its bifurcation is susceptible to vascular diseases. It is often analyzed using computational fluid dynamics (CFD) simulations, but it is challenging to prescribe boundary conditions that approach patient-specific flow conditions. We examined six boundary condition (BC) groups to determine the most accurate flow conditions aligning with available measured data. We conducted CFD simulations on a stenotic carotid bifurcation, using patient-specific Doppler ultrasound sonography velocity measurements at the inlet and both outlets. Three BC methods used defined inlet flow rate and either constant pressure (Basic), Windkessel model, or constant flow ratio (Murray) at the outlets. Three other methods were defined with flow rates at two boundaries and constant pressure at the third one. Defining two boundary flow rates shows the closest results to physiologically valid data. However, the difficult Doppler measurements on the outlet branches can inaccurately amplify velocity amplitudes and may detect a false flow direction. Therefore, cross-sectional corrections were implemented to fit the outlet and inlet flow rates, while keeping the measured velocity histories.Our results show that the Murray and Basic methods, while easily available, exclude carotid-specific flow conditions by disregarding downstream flow resistances. We conclude that a Windkessel-method can produce the most accurate results without forcing outflow conditions. However, usually unavailable measurements are necessary for its application. Simulations with outlet-defined volume flow can also produce physiologically valid solutions but require the application of cross-sectional geometry correction.

Spatial utilization of historical topographic map and its application in land reconstruction of ancient Chinese urban land use

The historical topographic map preserves rich geographic information and can provide direct assistance for the reconstruction of various geographic elements. Based on the historical data of cities throughout the Qing Dynasty, the land use scale data of cities across the country was obtained using GIS and urban perimeter conversion models. This study combines city information and city circumference records from the historical maps and archives of the late Qing Dynasty to quantitatively reconstruct the use patterns of ancient China’s urban land at a spatial resolution of 1° × 1°. Uncertainty analysis of the reconstruction results was conducted using modern remote sensing image data as the validation data set. The results showed the following. (1) During the late Qing Dynasty, the total area of urban land in the various provinces and regions was 1456.015 km2. The maximum value was 208.691 km2 in Beijing-Tianjin-Hebei, the minimum value was 1.713 km2 in Qinghai, and the average value was 56.001 km2. (2) The results of grid reconstruction show that among the 398 grids with urban land distribution, the maximum value is 64.099 km2/grid, the minimum value is 0.013 km2/grid, and the average value is 3.658 km2/grid. (3) Of all the grids with urban land, the urban land grid to the west of the Hu Line accounts for 12.5% and the east to 87.5%. (4) During the late Qing Dynasty, urban land use in China was primarily concentrated in agriculturally developed areas such as the North China Plain, the Central Plains, Jiangnan, and the Sichuan-Chongqing region. (6) The results of a kernel density estimation showed that there were obviously three core areas of urban land agglomeration in China during the late Qing Dynasty: the North China Plain-Central Plains, the Jiangsu-Shanghai-Zhejiang-Anhui area, and the Sichuan-Chongqing urban core area. This study provides basic data for urban land use during historical periods and provides a basis for the quantitative reconstruction of relevant urban land data for historical archives.

Comparison of the Symmetric and Asymmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Models in Forecasting the 2018-2023 Jakarta Composite Index

The Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) method assumes a homogeneous residual variance, but data with high volatility can cause violations of this assumption. Hence, it is interesting to compare the forecasting accuracy of symmetric and asymmetric Autoregressive Conditional Heteroskedasticity (ARCH) models in various data conditions. The research aimed to compare the accuracy of the symmetric ARCH/ Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and asymmetric TGARCH models in forecasting weekly Jakarta Composite Index (JCI) data on January 1st, 2018, to April 24th, 2023, by involving the influence of COVID-19 as a covariate variable and applying several validation scenario models to training and testing data. Based on the best-selected model, forecasting was carried out from May 1st, 2023, to July 3rd, 2023. The data used were weekly JCI opening data from January 1st, 2018, to April 24th, 2023, with the COVID-19 period as a covariate variable. The analysis results show that symmetric and asymmetric methods can handle violations of the heteroscedasticity assumption in the ARIMAX model. The best model produced based on four data validation scenarios is the asymmetric ARIMAX(3,1,3)-TGARCH(1,2) model with an average MAPE value of 3.158%. In this model, the COVID-19 variable significantly influences the JCI movement. Forecasting is done with forecasting results that are stable with confidence intervals that widen in each period.

Analisis Manajemen Pengangkutan TBS di PT. Serba Huta Jaya Desa Sumber Mulyo, Kecamatan Marbau, Kabupaten Labuhanbatu Utara, Sumatera Utara

This research aims to determine the management of transporting fresh palm fruit bunches (FFB) at PT. Serba Huta Jaya, analyzed the performance of transporting FFB from the harvesting site (TPH) to the palm oil mill (PKS), analyzing the obstacles in transporting FFB from TPH to PKS. The research method used in this research is mixed methods. According to Sugiyono (2015), mixed research methods are a research method between quantitative methods and qualitative methods to be used together in a research activity, so that more comprehensive, valid, reliable and objective data is obtained. The sample in this study was 10 FFB transport drivers and 20 loading and unloading workers. The results of this research indicate that FFB transportation management at PT. Serba Huta Jaya is still not doing well because there are frequent delays in FFB deliveries resulting in fruit being left on the trucks. There are several things that must be considered, namely road access for transporting FFB, the speed of harvest employees in cutting fruit, and damage to trucks in the field. The average performance of transporting FFB from TPH to PKS is 2.08 tonnes/hour and the average cost for one transport is Rp. 62.96/kg. The obstacles faced when transporting fruit from TPH to PKS were that the roads were still damaged at several points and the trucks were damaged while in the field.

Evaluation of the retrieved temperature and specific humidity from COSMIC-2 and FY-3D with Radiosonde, reanalysis data, and MetOp

The Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2), as the follow-on mission to COSMIC, commenced observations from 25 June 2019. COSMIC-2, with its high signal-to-noise ratio (SNR) and excellent stability, is expected to outperform COSMIC in detecting atmospheric parameters while penetrating the lower atmosphere.Therefore, to verify and analyze the detection capability of COSMIC-2 occultation in the neutral atmosphere, we retrieved temperature and specific humidity from COSMIC-2 and FY-3D based on the Able integral and the one-dimensional variational method. The evaluations are carried out on retrieval parameters at different altitudes using official RO products, radiosonde, reanalysis data, and MetOp. A detailed analysis of errors in the evaluation process is provided. The RMSEs for COSMIC-2 temperature and specific humidity are less than 1.5 K and 36 % respectively when radiosonde as validation data. Compared to FY-3D, accuracy improved by 11.1 % and 18.5 % respectively. FY-3D shows better consistency in the evaluation of the RO official products and radiosonde. Temporal and spatial analysis reveals minimal seasonal differences but significant latitude influence on evaluation outcomes. In the evaluation of reanalysis data, COSMIC-2 temperature and NCEP performed well in the troposphere, with a correlation coefficient greater than 0.9. The RMSE of the COSMIC-2 temperature and specific humidity in the MERRA-2 evaluation increased by 50.59 % and 16.65 % over FY-3D. Besides, derived parameters are more consistent with MetOp than radiosonde. Moreover, the co-localization threshold and resampling have less than 0.1 K and 5 % impact on the accuracy of evaluating temperature and specific humidity.

Meta-analysis of new intervention measures for preventing side effects of artificial joint replacement.

Intertrochanteric fracture of the femur occurs mostly among older people, and seriously affects daily life and quality of life. At present, physical intervention, drug treatment, routine intervention and rehabilitation training are widely used for prevention of side effects, but it is still inconclusive which intervention has the best effect. To compare the effects of new intervention measures for preventing side effects of artificial joint replacement. We searched the Chinese and English literatures for comparative studies on the prevention of side effects of new interventions for artificial joint replacement from July 2013 to June 2023 in China HowNet, PubMed, Wanfang, Weipu and other databases. Study quality was evaluated by improved Jadad scoring standard, and the effects of different interventions on preventing different complications were analyzed by meta-analysis of evidence-based medicine with Review Manager 5.0 software. Ten articles, including 869 cases, were finally included. The preventive effects of different interventions on the side effects of artificial joint replacement were studied, and valid data were extracted. There were two articles on the preventive effects of drug intervention, four on comparison of the preventive effects of combined and single interventions, and three on the preventive effects of physical intervention, rehabilitation training and routine intervention. Meta-analysis showed that the preventive effect of rivaroxaban was significantly better than low molecular weight heparin calcium [mean difference (MD) = -0.16, 95%CI: -0.28 to -0.04, P < 0.05]. The effect of combined intervention was significantly better than that of single intervention (MD = -0.08, 95%CI: -0.16 to -0.01, P < 0.001). Physical intervention was significantly better than routine intervention and rehabilitation training (MD = 0.26, 95%CI: 0.16-0.36, P < 0.001). Rivaroxaban combined with rehabilitation training is preferred for preventing deep vein thrombosis after artificial joint replacement. In the prevention of pulmonary embolism, rivaroxaban drug intervention is given priority. The effect of combined intervention is better than that of single intervention.

Freight logistics in Africa: information and technology as beacons

Movement of freight globally is complex due to the vast distances between countries, the in-transit perils, high costs, and challenges in meeting customer delivery timelines. Freight logistics within Africa is even more complex given the limited logistics infrastructure, varied regulations between countries, poor security and the high cost of transport. The purpose of this paper is to explore the status of academic literature on freight logistics in Africa and examine the potential of advanced information technologies in enhancing the logistics sector. The paper utilised the scoping literature review technique to select relevant documents. The freight logistics sector in Africa can be termed as struggling, it is among the least efficient globally. The lack of valid data on the various logistics components makes it difficult for stakeholders to plan for efficiency and effectiveness. The need for country specific technologies to address its unique challenges is emphasized. The smooth flow of goods across countries can only be achieved through collaboration among countries and investments in logistics infrastructure as well as technology. The study underscores the need for data-driven decision-making, context-specific technologies, and collaboration to improve Africa's freight logistics. The current study has explored the freight logistics literature focusing on Africa, not only to unearth the challenges, but also propose likely solutions. While the current study is limited to published literature, further research should empirically explore impactful technologies, compare countries' situations, and involve policymakers for comprehensive solutions.

A citizen science method to monitor a polyphagous shot hole borer infestation in Johannesburg's urban forest

Citizen science has become a powerful tool for gathering data, and the validity of these data should be tested. The accuracy of a citizen science database used to monitor the polyphagous shothole borer (PSHB) (Euwallacea fornicatus) (Coleoptera: Curculionidae: Scolytinae) infestation in a suburb of the city of Johannesburg was assessed. Out of 785 trees surveyed by the citizen scientists, 157 trees (20%) were selected as a validation data set to confirm the tree identity, tree health status and level of PSHB infestation. A chi-square (χ2) test was performed to examine whether there was a significant agreement between the citizen scientists’ and researcher’s data used for the classification of the tree infestation, tree health status and PSHB infestation holes. A Cohen’s kappa coefficient was calculated to determine the level of agreement between the two different sources of data, for the infested parts (trunk, main branches, and canopy branches) of the trees surveyed and PSHB infestation signs. The two surveys showed no significance difference for classifying tree health status and PSHB infestation holes. There was a high level of agreement between citizen scientists and the researcher ratings for measures of PSHB infestation (kappa values ranging between 0.71 and 0.89) and the level of agreement was significant. In conclusion, citizen science allowed us to obtain a large quantity of data at a low cost that correlated with the researchers’ validation dataset. Citizen science is a useful method for monitoring PSHB in a diverse urban forest in trees that show obvious signs of infestation.

Optimizing Productive Waqf Management to Achieve Sustainable Development Goal's (Study of the Indonesian Waqf Board (BWI) Representative of Lampung Province)

This research involves field research using a descriptive qualitative approach. The data collection techniques include observation, interviews, and documentation. Subsequently, data analysis was conducted through stages of data collection, data reduction, data display, and conclusion drawing. Data validity is ensured through source triangulation and technical triangulation. The results showed that the productive waqf managed by BWI Lampung Province is in the form of cash waqf. The management of cash waqf is carried out through three stages: collection, management, and distribution. BWI’s steps in optimizing productive waqf management involve improving BWI’s management, increasing public education about cash waqf, enhancing the use of digital technology, fostering cooperation with various stakeholders, and enhancing the competence of the nazir. Additionally, the contribution of BWI Lampung Province to the SDGs is evident in the achievement of three goals: no hunger, quality education, and decent work and economic growth. Keywords: productive Waqf, sustainable development goal’s, Indonesian Waqf Board

Constructed wetlands as nature-based solutions in managing per-and poly-fluoroalkyl substances (PFAS): Evidence, mechanisms, and modelling

Per- and poly-fluoroalkyl substances (PFAS) have emerged as newly regulated micropollutants, characterised by extreme recalcitrance and environmental toxicity. Constructed wetlands (CWs), as a nature-based solution, have gained widespread application in sustainable water and wastewater treatment and offer multiple environmental and societal benefits. Despite CWs potential, knowledge gaps persist in their PFAS removal capacities, associated mechanisms, and modelling of PFAS fate. This study carried out a systematic literature review, supplemented by unpublished experimental data, demonstrating the promise of CWs for PFAS removal from the influents of varying sources and characteristics. Median removal performances of 64, 46, and 0 % were observed in five free water surface (FWS), four horizontal subsurface flow (HF), and 18 vertical flow (VF) wetlands, respectively. PFAS adsorption by the substrate or plant root/rhizosphere was deemed as a key removal mechanism. Nevertheless, the available dataset resulted unsuitable for a quantitative analysis. Data-driven models, including multiple regression models and machine learning-based Artificial Neural Networks (ANN), were employed to predict PFAS removal. These models showed better predictive performance compared to various mechanistic models, which include two adsorption isotherms. The results affirmed that artificial intelligence is an efficient tool for modelling the removal of emerging contaminants with limited knowledge of chemical properties. In summary, this study consolidated evidence supporting the use of CWs for mitigating new legacy PFAS contaminants. Further research, especially long-term monitoring of full-scale CWs treating real wastewater, is crucial to obtain additional data for model development and validation.

Effects of Modified Schema-Based Instruction to Teach Students With Autism to Solve Additive Compare Problems

This study assessed the effectiveness of modified schema-based instruction (MSBI) in teaching students with autism spectrum disorder (ASD) how to solve additive compare word problems. The study was conducted in Spain, and it involved three students with ASD aged 6, 7, and 9. A single-case, multiple-baseline across-students design was followed. Results indicated a functional relation between MSBI and improved performance in solving additive compare problems of type difference amount unknown and compare amount unknown, maintained for 4 to 6 weeks post-intervention. Additionally, two students successfully applied their skills to reference-amount-unknown compare problems, demonstrating successful generalization. However, their generalization to problems involving two operations was somewhat limited by difficulties with one of the operations. Social validity data collected from the students’ families and teachers indicated that both groups had positive views on the use of the MSBI for mathematical word problem-solving skills.

Development and first-stage validation of a digital version of the Digit Symbol Substitution test for use in assessing cognitive function in older people with diabetes.

To describe the development and report the first-stage validation of a digital version of the digit symbol substitution test (DSST), for assessment of cognitive function in older people with diabetes. A multidisciplinary team of experts was convened to conceptualize and build a digital version of the DSST and develop a machine-learning (ML) algorithm to analyse the inputs. One hundred individuals with type 2 diabetes (aged ≥ 60 years) were invited to participate in a one-time meeting in which both the digital and the pencil-and-paper (P&P) versions of the DSST were administered. Information pertaining to demographics, laboratory measurements, and diabetes indices was collected. The correlation between the digital and P&P versions of the test was determined. Additionally, as part of the validation process, the performance of the digital version in people with and without known risk factors for cognitive impairment was analysed. The ML model yielded an overall accuracy of 89.1%. A strong correlation was found between the P&P and digital versions (r = 0.76, p < 0.001) of the DSST, as well as between the ML model and the manual reading of the digital DSST (r = 0.99, p < 0.001). This study describes the development of and provides first-stage validation data for a newly developed digital cognitive assessment tool that may be used for screening and surveillance of cognitive function in older people with diabetes. More studies are needed to further validate this tool, especially when self-administered and in different clinical settings.