Miracle-dz/UniFAR-FTUs huggingface dataset:
Alternative assessment challenges of teachers in inclusive senior high schools in Northern Ghana

The paper presents findings of teachers’ challenges in using alternative assessment procedures to assess learners with visual impairment in two purposively sampled inclusive senior high schools in the Northern part of Ghana with particular reference to Sirugu and Wa Senior High Schools in the Upper East and Upper West Regions, respectively. The study’s population comprised teachers in inclusive senior high schools in Ghana. Experts in authors’ institutions reviewed and validated the content and construct validity of the instrument. The reliability was established by pilot testing the instrument using respondents from Wenchi Methodist Senior High School, an inclusive school in the Bono Region of Ghana. The Cronbach’s coefficient alpha derived for the questionnaire was 0.79. The study used a cross-sectional descriptive survey design and a sample size of 136 teachers selected using the simple random sampling technique. Data were collected using the authors’ self-developed questionnaire after pilot-testing for its refinement. Descriptive statistics (mean and standard deviation) were used to analyse the data. The findings revealed that most teachers had constraints using alternative assessment methods to evaluate learners who are visually impaired in the classroom due to their inadequate expertise. Overcrowded classrooms leading to large teacher-learner ratios, lack of assessment materials and logistics also posed formidable challenges to using alternative assessments. The study recommends the expansion of existing infrastructure and equipping teachers in inclusive schools with the requisite skills and competencies to use alternative assessment methods effectively. It is also imperative for the government and stakeholders to provide the required logistics to facilitate effective teaching, learning and assessment of learners with visual impairment in schools.
Photocatalytic Removal of Anthracene using Zinc Oxide Nanoparticles Synthesized by Fusarium proliferatum WC416

Abstract Release of complex waste in environment due to massive industrialization is a major threat globally. Anthracene is a polycyclic aromatic hydrocarbon generally released as a waste from petrochemical industries. It may bioaccumulate in food chain which results into nephrotoxicity in humans. Thus, there is a necessity to clean up the contaminated site efficiently. Bioremediation is ecofriendly technique for waste reclamation however it consumes more time. Thus, nanotechnology has exploded with unique properties of nanomaterials. Bio-based nanoparticles (NPs) synthesis is a recent and agro ecologically friendly approach for remediating environmental waste. For the first time present study reports Fusarium proliferatum WC416 isolated from refinery effluent for the synthesis of zinc oxide nanoparticles (ZnO NPs). Moreover, biosynthesized ZnO NPs used for their photocatalytic efficiency to remove anthracene at varying concentration. During synthesis of ZnO in aqueous medium showed a characteristic peak at 365 nm as analyzed by Ultraviolet–visible spectrum. The formation of NPs was further confirmed by Fourier transform infrared spectroscopy (FTIR), transmission electron microscope (TEM), and energy-dispersive x-ray (XRD). Morphologically the synthesized ZnO NPs were found to be in irregular polygonal shapes. Moreover, ZnO NPs showed photocatalytic activity for removal of anthracene upto 65 ± 0.03%, p value = 0.00032 at 100 ppm followed by 50.7% ± 0.02%, p value = 0.00023 at 250 ppm and 40.83 ± 0.03%, p value = 0.00042 at 500 ppm within 6 hr duration. Anthracene transformation during photocatlytic activity was confirmed by GC MS analysis, and the products were detected as 1,2 benzene carboxylic acid (m/z 166) and salicylic acid (m/z 138). Phytotoxicity effect of synthesized ZnO NPs on seed germination index of Phaseolus moonga and Vigina radiata was also studied to promote sustainable and biocompatible nano-agriculture.
Switzerland's INDC: first in, but room for improvement

 Switzerland is the first government to formally submit an INDC to the UNFCCC. It aims at halving greenhouse gas emissions by 2030 compared to 1990 levels, with at least a 30% reduction by 2030 domestically. The remainder of the emission reductions would come from “emissions reduction measures abroad”.  Switzerland’s proposal includes forestry accounting, which prevents an unambiguous quantification of its target, and will likely result in weaker emissions reductions across all other sectors.  The INDC is in line only with the least-stringent equity proposals.  Switzerland’s INDC includes the option of fulfilling a substantial share of its 50% reduction by 2030 through emissions reduction measures undertaken in other countries. The expenditure to achieve this could range from virtually zero to USD$1.3 billion.  To bring Switzerland’s INDC in line with fair emissions reductions that are 2 ̊C compatible, it needs to increase its target and clarify and limit the role of abatement in its LULUCF sector.
Theory and Applications of Time Moment Analysis to Study the Fate of Reactive Solutes in Soil

As concern for both the quality and the conservation of soil and water resources increases, there is a greater demand for understanding the fate and transport of water and solutes as they move through natural field soils. A major obstacle in assessing the fate of solutes in natural field soils is the field-scale heterogeneity exhibited by the transport properties. Over the last two decades, theoretical developments have been advanced to incorporate spatially variable solute dispersivities (Gelhar and Axness 1983; Dagan 1984), solute retardation (Garabedian et al. 1988; Miralles-Wilhelm and Gelhar 1996a; Espinoza and Valocchi 1997, 1998), and solute decay coefficients (Kabala and Sposito 1991; Miralles-Wilhelm and Gelhar 1996b) into transport models. Large-scale field experiments conducted at the Borden site (Freyberg 1986; Sudicky 1986), one near Columbus, Mississippi (Boggs et al. 1992; Adams and Gelhar 1992), and at Cape Cod (Garabedian et al. 1991; LeBlanc et al. 1991) demonstrated that macrodispersivity may be predicted from spatially variable hydraulic conductivity using stochastic theory. Although additional experiments and, more importantly, carefully designed and controlled experiments are needed (Gelhar 1997), results of previous field-scale transport studies suggest that a stochastic framework may be helpful in addressing subsurface environmental problems encountered in natural field situations.
Development of YOLO-based Model for Fall Detection in IoT Smart Home Applications

—In smart home applications, effective fall detection is a critical concern to minimize the occurrence of falls leading to injuries, especially for the assistance of elderly individuals. Various methods have been proposed, including both vision-based and non-vision-based approaches. Among these, vision-based approaches have garnered significant attention from researchers due to their practicality and applicability. However, existing vision-based methods face challenges such as low accuracy rates and high computational costs, which still need further exploration to enhance fall detection effectiveness. This study aims to develop a vision-based fall detection system tailored for smart home care applications. The objective of this study is to develop an accurate and lightweight fall detection method that is applicable in IoT platforms. A You Only Look Once (YOLO) based network is trained and tested to identify human falls accurately. The experimental results demonstrate that the developed YOLO-based technique shows promising outcomes for human fall detection and holds potential for integration in the Internet of Things (IoT) enabled smart home applications.
Research on Anti-Range Migration Sparse Reconstruction Algorithm in Target Parameter Estimation of Frequency-Agile Radar

The frequency agile radar (FAR) target parameter estimation scenario based on sparse reconstruction faces the problem of high-resolution range migration (HRRM), which corresponds to phase interference that matches the target atoms in the dictionary matrix and forms a pseudopeak structure. In this article, we propose an anti-range migration sparse reconstruction (ARMSR) algorithm for recovering the range-Doppler parameters of high-velocity targets. The main feature of this algorithm is its hybrid processing structure, which introduces 1-D high-resolution IFFT processing into the sparse recovery model. Based on the range imaging gain compensated by different Doppler phases, the velocity index set is preliminarily determined, and then the compensation function for the range migration phase is calculated. Using the measurement vector compensated by the migration component as input, a subdictionary matrix was constructed at super-resolution intervals in the neighborhood of the range profile and velocity index set. The proposed algorithm effectively overcomes the influence of range migration components and reduces pseudo peaks in conventional sparse reconstruction planes. In addition, the proposed algorithm performs parameter estimation in a high-resolution neighborhood space, resulting in improved computational efficiency and resolution of target parameters. The simulation results demonstrate that the proposed algorithm has significant reconstruction advantages in scenarios with high-velocity targets and super-resolution.
Mass Media and Reproductive Behavior in Pakistan, India, and Bangladesh

This paper reports results of the Demographic and Health Surveys in Pakistan India and Bangladesh between 1990 and 1997 to determine the effect of media exposure on family planning attitudes and behavior. Variables such as socioeconomic educational and cultural status were also included in the study. Practice of family planning methods was seen to significantly increase with exposure to different forms of media in Pakistan from 4 of 16 respondents in the 1990-91 survey to 12 of 14 respondents in the 1994-95 survey and more women declared their intent to use contraception from 1990-91 to 1994-95. The relationship between exposure to mass media and contraceptive use is strong in India except for a modest increase in Northern India during the survey conducted in 1993. In Bangladesh a comprehensive family planning has been established. The survey reveals that women exposed to different forms of media were twice likely to approve family planning practices. Effect of radio as an effective media of family planning information had declined from 22 of 32 respondents in 1993-94 survey to 17 of 32 in the 1996-97 survey. A strong statistical association between media exposure especially through television and varied reproductive behavior was found to exist even in the midst of socioeconomic residential life-cycle covariates.
Research progress on gas chromatography columns

With the advancement of analytical technology and chromatographic technology, abundant research results have been obtained in the preparation and improvement of gas chromatographic columns. The development of microelectromechanical system (MEMS) technology provides an effective processing technology for the manufacture of micro gas chromatographic column chips. This paper reviews the improvement of different types of gas chromatography columns, including large diameter, thick liquid film, micro-packed, cluster, annular and fiber-packed columns, etc., as well as the research progress of micro gas chromatography column chips based on MEMS technology. Such as microchannel processing technology, column structure, chip substrate material and preparation method of stationary phase. These advances provide an effective way to obtain micro-GC column chips with better separation performance.
An Efficient Method for Implementing Applications of Smart Devices Based on Mobile Fog Processing in a Secure Environment

—Smart technology and the Internet of Things (IoT) are advancing and growing daily in the modern world. The demand for solutions to execute complex applications and protect user security and privacy increases as the number of smart devices in our surroundings increases. Mobile fog processing aids us in this situation by providing a fresh and effective method for running smart device applications in a safe setting. Due to the delay and high volume of requests, the centralized and traditional architecture of cloud processing cannot handle the high user demand and effectively implement delay-sensitive and real-time programs. To address these issues, a virtual mobile fog processing architecture that establishes a layer between mobile apps and the cloud layer was developed in this work. In this layer, storage, processing, and encrypted communication occur on separate nodes not connected to the cloud. These nodes are virtually implemented on a single server. An Android smart system-based augmented reality application that uses a marker to display dynamic 3D objects has been introduced. Its functioning has been assessed in both the cloud-based architecture and the suggested architecture in two 4G and telecom mobile internet networks. The evaluation findings demonstrate the suggested architecture's superior performance in both communication networks. The suggested mobile fog-based architecture makes use of the Internet of Telecommunications to create high-volume 3D models quickly and to the satisfaction of a real-time application. In addition to these accomplishments, the results demonstrate that the suggested architecture outperforms typical cloud-based architecture in terms of lowering overall energy consumption by up to 34%.
Estimation of Acacia tortilis tree attributes in Bou-Hedma, Tunisia: an object-based approach

Acacia tortilis subsp. raddiana is an important woody species in the pre-Saharan Tunisia zone. The species is able to tolerate extreme drought (in the range of 20 to 200 mm), through special adaptations such as a deep lateral root and partial repelling of leafs in the dry season. It is the only forest tree persisting on the edge of the desert and is therefore considered a keystone species (Abdallah et al., 2008). The restoration of the original woodland combating desertification, particularly by afforestation and reforestation, is therefore an important research activity in the Bou-Hedma region. The restoration goes hand in hand with a climate change in the Biosphere Reserve. The impact of this local climate change is also felt outside the Biosphere Reserve, where the local rural population depends on rain fed agriculture and the water resources originating from water infiltrated on the mountains of the Bou-Hedma mountain chain. This change in climate conditions has been directly observable on the mountain chain, where Stipa tenacissima (Poaceae) now grows without human intervention. However, scientific documentation remains scarce. In order to study the different effects of woodland restoration in Bou-Hedma, the number of Acacia trees (individuals and tree groups) and their attributes need to be accurately estimated, and that is exactly the focus our study.
Effectiveness Of Learning Management System In University Of Science And Technology Of Southern Philippines Cagayan De Oro And Villanueva Campuses: A Policy Recommendation

Learning Management Systems (LMS) is gaining momentum in the Philippines as educational institutions and businesses seek to provide flexible, accessible, and cost-effective training solutions. The Commission on Higher Education mandates the adoption of LMS in higher education institutions. This is based on CHED Memorandum Order No. 04, Series of 2020, which outlines the policies, standards, and guidelines for the establishment and operation of e-learning programs in HEIs. The government's push towards digital education and the growing adoption of cloud-based solutions are driving the growth of LMS in the country. However, there are still several challenges that need to be addressed, particularly in terms of digital infrastructure and teacher training and support. This study examined e-learning in higher education and motivation as variables that affect student learning. The study involved one hundred fifty-seven (157) university students from the University of Science and Technology of Southern Philippines Cagayan de Oro and Villanueva campuses. Data collection was assisted by using a questionnaire with a five-point Likert scale that tested its validity and reliability. The study showed the importance of user-friendly LMS interfaces and technical support, and the crucial role of faculty members in supporting and enriching the learning experience through the LMS.
Data Augmentation of Sensor Time Series using Time-varying Autoregressive Processes

This work presents a novel data-centric solution for fault diagnostics and failure prognostics consisting of a data-augmentation method which is well suited for non-stationary mutivariate time-series data. The method, based on time-varying autoregressive processes, can be employed to extract key information from a limited number of samples and generate new artificial samples in a way that benefits the development of diagnostics and prognostics solutions. The proposed approach is tested based on three real-world datasets associated with failure diagnostics problems using two types of machine learning methods. Results indicate the proposed method improves performance in all tested cases.
Oropharyngeal free flap reconstruction: Transoral robotic surgery versus open approach

Transoral robotic surgery (TORS) has evolved since its 2009 US Food and Drug Administration approval for use in local stage T1–T2 oropharyngeal carcinoma. The ability to resect increasingly larger and more complex lesions has led to the need to introduce reconstructive techniques through this route, avoiding the classic transmandibular or pull‐through approach. Few studies have compared the safety, efficacy, and advantages of TORS versus classic open approaches in oropharyngeal salvage surgery with reconstruction using microanastomosed flaps. Here we retrospectively compare our center's experience with the open approach and TORS and describe the technical variations used.
SRSF-GAN: A Super-Resolution-Based Spatial Fusion With GAN for Satellite Images With Different Spatial and Temporal Resolutions

Recently, spatiotemporal fusion technologies have been rapidly developed and widely applied, which generally require one or more pairs of coarse- and fine-resolution images as reference data and a coarse-resolution image at the prediction time to produce a fine-resolution image at the forecast time. Consequently, most spatiotemporal fusion methods are phase-based and obtain temporal changing information from the coarse-resolution image pairs. Consequently, they usually have rigid constraints on reference data selection using the temporal interval criterion. However, due to the relatively long revisit cycle and cloud contamination, it is difficult to prepare adequate high-quality reference data with little change, especially for large-scale fusion tasks. Therefore, we propose a spatial-based fusion method that only requires the coarse images at the prediction time and a fine reference image selected by a spatial–spectral-similarity criterion, named super-resolution-based spatial fusion with the generative adversarial network (SRSF-GAN). SRSF-GAN uses a super-resolution (SR) module merely on the coarse images at the prediction time and then performs a multiscale fusion with the reference fine image. Moreover, the spatial attention mechanism is adopted to achieve dynamic weight tuning, i.e., assigning more weight to the SR image for changed areas and more weight to the fine reference image for unchanged areas. Comparison experiments based on three datasets show that our model can outperform the state-of-the-art methods, and the changes with different spatial and spectral ranges of variation can be recovered. The code will be uploaded to the following website: https://github.com/Zhaosir996/SRSFGAN.
Analysis of a Four-Component Competing Endogenous RNA Network Reveals Potential Biomarkers in Gastric Cancer: An Integrated Systems Biology and Experimental Investigation

Background: Gastric cancer (GC) is a common and deadly cancer worldwide. Molecular changes underlying the development of GC are not thoroughly understood. Therefore, we constructed and analyzed a novel four-component competing endogenous RNA (ceRNA) network to introduce plausible diagnostic and prognostic biomarkers in GC. Materials and Methods: Transcriptomics and circular RNA (circRNA) data were retrieved from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, respectively. After batch effect correction, differential expression analysis, and interaction prediction, a ceRNA network including long noncoding RNAs (lncRNAs), circular RNAs (circRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) was established. Enrichment analyses were performed, and a protein–protein interaction (PPI) network was constructed. Furthermore, a subnetwork was extracted, and using the quantitative real-time polymerase chain reaction (qRT-PCR) method, the expression changes of two hub ceRNAs were examined. Finally, survival analysis was performed to identify potential prognostic RNAs. Results: A four-component ceRNA network containing 822 nodes and 1365 edges was constructed. Enrichment analyses unveiled important signaling pathways and gene ontologies such as neuroactive ligand–receptor interaction and axonogenesis. The PPI network showed the interactions among mRNAs of the ceRNA network. qRT-PCR indicated downregulation of EPHA5 and SNAP91 mRNAs in GC compared to control tissues. Survival analyses revealed eight mRNAs and one lncRNA as potential prognostic biomarkers in GC. Conclusion: The established four-component network of ceRNAs in GC reveals a comprehensive view of the molecular and cellular characteristics of GC progression, which can be considered as a basis to examine and validate potential diagnostic and prognostic biomarkers as well as therapeutic targets.
Effects and challenges to implement differentiated mathematics teaching among fourth graders in Montenegro

In this quasi-experimental pretest-posttest study we examined the effects of differentiated instruction (DI) in within-class ability groupings of 246 Montenegrin fourth-graders and their ability to solve algebraic equations. We assessed 2 parallel student groups at equal achievement levels to compare DI, in which teaching and work modes were adapted to students’ grouping according to previous achievement and pretest scores, and traditional whole-class instruction. Pretest-posttest evaluations were administered to both groups, and observation indicators were evaluated to assess the level and type of student activities, engagement, and individualisation. Students in the homogeneous DI experimental groups with tailored instructions were significantly more successful at solving algebraic tasks than their peers in the traditional whole-class instruction control group. DI improved students’ results, but teachers required specific training and significantly more preparation time.
SALINITY STRESS IN AGRICULTURAL LAND: CHALLENGES AND OPPORTUNITIES

Plants have a series of fine mechanisms for responding to environmental stresses. These mechanisms include perception of stress, molecular and physiological response to stress which may results into the adaptation to that stress. In all the stresses, salinity stress is a major concern in today's agriculture. Recent experimentation with transgenic plants has led to increased understanding of salinity tolerance, with emphasis on the areas of ion homeostasis, osmotic regulation and antioxidant protection. A case study of the major challenges and opportunities to improve stress tolerance in plants under salinity is presented. As different abiotic stresses are inter-related (e.g. salinity and osmotic stress), our ability to improve crop performance may well be determined by combining different, apparently unrelated approaches for introducing several stress tolerance mechanisms into specific crop plants.