Scientific Applications Research Articles

Image Detection Network Based on Enhanced Small Target Recognition Details and Its Application in Fine Granularity

Image detection technology is of paramount importance across various fields. This significance is not only seen in general images with everyday scenes but also holds substantial research value in the field of remote sensing. Remote sensing images involve capturing images from aircraft or satellites. These images typically feature diverse scenes, large image formats, and varying imaging heights, thus leading to numerous small-sized targets in the captured images. Accurately identifying these small targets, which may occupy only a few pixels, is a challenging and active research area. Current methods mainly fall into two categories: enhancing small target features by improving resolution and increasing the number of small targets to bolster training datasets. However, these approaches often fail to address the core distinguishing features of small targets in the original images, thus resulting in suboptimal performance in fine-grained classification tasks. To address this situation, we propose a new network structure DDU (Downsample Difference Upsample), which is based on differential and resolution changing methods in the Neck layer of deep learning networks to enhance the recognition features of small targets, thus further improving the feature richness of recognition and effectively solving the problem of low accuracy in small target object recognition. At the same time, in order to take into account the recognition effect of targets of other sizes in the image, a new attention mechanism called PNOC (protecting the number of channels) is proposed, which integrates small target features and universal object features without losing the number of channels, thereby increasing the accuracy of recognition. And experimental verification was conducted on the PASCAL-VOC dataset. At the same time, it was applied to the testing of the fine-grained MAR20 dataset and found that the performance was better than other classic algorithms. At the same time, because the proposed framework belongs to a one-stage detection method, it has good engineering applicability and scalability, and universality in scientific research applications are good. Through comparative experiments, it was found that our algorithm improved the performance of the mAP by 0.7% compared to the original YOLOv8 algorithm.

Exploring Clique Transversal Problems for d-degenerate Graphs with Fixed d: From Polynomial-Time Solvability to Parameterized Complexity

This paper explores the computational challenges of clique transversal problems in d-degenerate graphs, which are commonly encountered across theoretical computer science and various network applications. We examine d-degenerate graphs to highlight their utility in representing sparse structures and assess several variations of clique transversal problems, including the b-fold and {b}-clique transversal problems, focusing on their computational complexities for different graph categories. Our analysis identifies that certain instances of these problems are polynomial-time solvable in specific graph classes, such as 1-degenerate or 2-degenerate graphs. However, for d-degenerate graphs where d≥2, these problems generally escalate to NP-completeness. We also explore the parameterized complexity, pinpointing specific conditions that render these problems fixed-parameter tractable.

Arabic Music Genre Identification

Music Information Retrieval (MIR) is one data science application crucial for different tasks such as recommendation systems, genre identification, fingerprinting, and novelty assessment. Different Machine Learning techniques are utilised to analyse digital music records, such as clustering, classification, similarity scoring, and identifying various properties for the different tasks. Music is represented digitally using diverse transformations and is clustered and classified successfully for Western Music. However, Eastern Music poses a challenge, and some techniques have achieved success in clustering and classifying Turkish and Persian Music. This research presents an evaluation of machine learning algorithms' performance on pre-labelled Arabic Music with their Arabic genre (Maqam). The study introduced new data representations of the Arabic music dataset and identified the most suitable machine-learning methods and future enhancements.

Using Smartphones for English Speaking Skill Development from the Omani EFL Learners' Perspectives

The widespread use of hand-held devices, including smartphones, has been invested in language learning education around the world. This study intends to inspect Omani EFL learners' perspectives on using smartphones to develop their speaking skills. To answer the research questions, the researchers used a 5-point Likert questionnaire to collect data from 259 Omani EFL students from the English Language Unit, Preparatory Studies Center, University of Technology and Applied Sciences (UTAS) in Salalah, Sultanate of Oman. Findings showed positive beliefs about using smartphones. The participants stated that they sometimes use smartphones for oral skills. In the study, a significant difference between female and male students in terms of perceptions towards smartphones was evident, in addition to a positive correlation between the students' levels of study, perceptions, and uses of smartphones. In light of the results, the study provides invaluable recommendations and pedagogical implications for students, teachers, and syllabi designers.

Omani Students’ Familiarity with IELTS-based Reading Exam Format and Strategies: A Qualitative Phenomenological Study

This study aims to investigate Omani students’ familiarity with the IELTS-based reading exam’s time limit, format, and reading strategies. It also aims to explore the extent to which Omani students adopt independent learning strategies and the challenges they face while taking the IELTS-based reading exams. Omani students need help with their reading skills, as is shown in their below-average scores in IELTS or IELTS-based reading exams conducted by the British Council and the University of Technology and Applied Sciences (UTAS-Ibra). Hence, this study seeks to answer the research questions of whether Omani students studying at level four are familiar with the IELTS-based reading exam’s time limit, format, and reading strategies, and adopt independent learning strategies. The study employed a qualitative research method, and data was collected from eight students (N=8) studying at level four in the preparatory studies center of UTAS-Ibra. This study is significant because it has implications for the stakeholders. The study results show that participants generally possessed a positive comprehension of the time constraints and overall structure of the Level 4 IELTS-based reading exam. Nevertheless, a notable distinction emerged between high and moderate achievers in utilizing reading strategies, proficiency in comprehending complex vocabularies, and sustained engagement in independent learning. To address the challenges high and moderate achievers face, the study suggests implementing specific interventions tailored to their needs.

Enhanced trapping properties of coupled plasmonic tweezers via plasmon-exciton interaction

Excited plasmonic nanoantennas enable the manipulation of photons coupled with quantum emitters or the trapping of particles as plasmonic tweezers, leveraging the strong evanescent gradient fields at the nanoscale. However, the ohmic loss of metals presents a significant challenge for the stable and high-precision manipulation of nanoparticles without causing damage. In this study, we investigated the enhanced trapping properties induced by plasmon-exciton interaction for coupled plasmonic tweezers. Through the coupling between plasmons and excitons, dynamic particle trapping is achievable under low excitation power conditions of 0.45 mW, with the trapping stiffness increasing by nearly 20 times. Furthermore, the trapping stiffness can be fine-tuned by modulating the quantity of excitons to regulate the coupling strength. Coupled plasmonic tweezers offer an effective strategy to mitigate the influence of ohmic loss on trapping performance, by manipulating particles with minimal laser power. These findings provide insights into enhancing trapping performance through plasmon-exciton coupling, with potential applications in biomedicine and quantum information science.

Exploring DNA Analysis Methods and Genetic Research Applications in Low and Middle-Income Nations: A Study of Sri Lanka

Using Sri Lanka as a case study, the paper explores how DNA analysis has transformed genetic research, particularly in low and middle-income countries (LMICs). It discusses various DNA analyzing techniques, from traditional methods like Sanger sequencing to advanced techniques such as next-generation sequencing (NGS) and CRISPR-Cas9, highlighting their applications in disease research, population genetics, and forensic science. Sri Lanka's advancements in genetic research, including DNA sequencing, typing, and recent developments in X-chromosome-based DNA typing, are emphasized. The paper also examines challenges and opportunities in LMICs regarding genetic research and underscores the importance of DNA analysis in advancing personalized medicine and understanding genetic diversity. Additionally, it discusses Sri Lanka's efforts in education and training in molecular biology. It explores the country's rich genetic diversity and demographic history, focusing on ethnic studies and historical interactions among different population groups. Overall, the paper highlights the significance of DNA analysis in genetic research and its potential implications for LMICs. Sri Lanka is a notable example of progress in the field.

Research on the Current Situation and Calculation Method of Carbon Emissions Assessment for Building Curtain Walls

Curtain wall systems stand out as a pivotal domain within the construction sector’s endeavors towards energy efficiency and carbon mitigation. To refine the evaluation framework for carbon emissions within this industry, this paper explores the calculation and assessment method for building curtain walls. The article first reviews the current research status regarding carbon emissions from materials and the impact of curtain walls on buildings in the operational stage. Based on lifecycle theory, the carbon emissions from building curtain walls are divided into six stages: material acquisition, processing and production, installation and construction, transportation, use and maintenance, and dismantling. On this basis, this paper proposes a method for calculating carbon emissions from building curtain walls. Following that, a case study is conducted using a specific glass curtain wall project for illustrative analysis. The results indicate that the carbon emissions from the material acquisition stage constitute approximately 90% of the total, serving as the primary source of carbon emissions for glass curtain walls. Furthermore, the scientific application of photovoltaics can significantly reduce the carbon emission levels of building curtain walls. Finally, an analysis was conducted on the current issues existing in the evaluation of carbon emissions.

Assessment of Accuracy of Moderate-Resolution Imaging Spectroradiometer Sea Surface Temperature at High Latitudes Using Saildrone Data

The infrared (IR) satellite remote sensing of sea surface skin temperature (SSTskin) is challenging in the northern high-latitude region, especially in the Arctic because of its extreme environmental conditions, and thus the accuracy of SSTskin retrievals is questionable. Several Saildrone uncrewed surface vehicles were deployed at the Pacific side of the Arctic in 2019, and two of them, SD-1036 and SD-1037, were equipped with a pair of IR pyrometers on the deck, whose measurements have been shown to be useful in the derivation of SSTskin with sufficient accuracy for scientific applications, providing an opportunity to validate satellite SSTskin retrievals. This study aims to assess the accuracy of MODIS-retrieved SSTskin from both Aqua and Terra satellites by comparisons with collocated Saildrone-derived SSTskin data. The mean difference in SSTskin from the SD-1036 and SD-1037 measurements is ~0.4 K, largely resulting from differences in the atmospheric conditions experienced by the two Saildrones. The performance of MODIS on Aqua and Terra in retrieving SSTskin is comparable. Negative brightness temperature (BT) differences between 11 μm and 12 μm channels are identified as being physically based, but are removed from the analyses as they present anomalous conditions for which the atmospheric correction algorithm is not suited. Overall, the MODIS SSTskin retrievals show negative mean biases, −0.234 K for Aqua and −0.295 K for Terra. The variations in the retrieval inaccuracies show an association with diurnal warming events in the upper ocean from long periods of sunlight in the Arctic. Also contributing to inaccuracies in the retrieval is the surface emissivity effect in BT differences characterized by the Emissivity-introduced BT difference (EΔBT) index. This study demonstrates the characteristics of MODIS-retrieved SSTskin in the Arctic, at least at the Pacific side, and underscores that more in situ SSTskin data at high latitudes are needed for further error identification and algorithm development of IR SSTskin.

Multi-objective Bayesian active learning for MeV-ultrafast electron diffraction

Ultrafast electron diffraction using MeV energy beams(MeV-UED) has enabled unprecedented scientific opportunities in the study of ultrafast structural dynamics in a variety of gas, liquid and solid state systems. Broad scientific applications usually pose different requirements for electron probe properties. Due to the complex, nonlinear and correlated nature of accelerator systems, electron beam property optimization is a time-taking process and often relies on extensive hand-tuning by experienced human operators. Algorithm based efficient online tuning strategies are highly desired. Here, we demonstrate multi-objective Bayesian active learning for speeding up online beam tuning at the SLAC MeV-UED facility. The multi-objective Bayesian optimization algorithm was used for efficiently searching the parameter space and mapping out the Pareto Fronts which give the trade-offs between key beam properties. Such scheme enables an unprecedented overview of the global behavior of the experimental system and takes a significantly smaller number of measurements compared with traditional methods such as a grid scan. This methodology can be applied in other experimental scenarios that require simultaneously optimizing multiple objectives by explorations in high dimensional, nonlinear and correlated systems.

Reducing training data needs with minimal multilevel machine learning (M3L)

For many machine learning applications in science, data acquisition, not training, is the bottleneck even when avoiding experiments and relying on computation and simulation. Correspondingly, and in order to reduce cost and carbon footprint, training data efficiency is key. We introduce minimal multilevel machine learning (M3L) which optimizes training data set sizes using a loss function at multiple levels of reference data in order to minimize a combination of prediction error with overall training data acquisition costs (as measured by computational wall-times). Numerical evidence has been obtained for calculated atomization energies and electron affinities of thousands of organic molecules at various levels of theory including HF, MP2, DLPNO-CCSD(T), DFHFCABS, PNOMP2F12, and PNOCCSD(T)F12, and treating them with basis sets TZ, cc-pVTZ, and AVTZ-F12. Our M3L benchmarks for reaching chemical accuracy in distinct chemical compound sub-spaces indicate substantial computational cost reductions by factors of ∼1.01, 1.1, 3.8, 13.8, and 25.8 when compared to heuristic sub-optimal multilevel machine learning (M2L) for the data sets QM7b, QM9 LCCSD(T) , Electrolyte Genome Project, QM9 AECCSD(T) , and QM9 EACCSD(T) , respectively. Furthermore, we use M2L to investigate the performance for 76 density functionals when used within multilevel learning and building on the following levels drawn from the hierarchy of Jacobs Ladder: LDA, GGA, mGGA, and hybrid functionals. Within M2L and the molecules considered, mGGAs do not provide any noticeable advantage over GGAs. Among the functionals considered and in combination with LDA, the three on average top performing GGA and Hybrid levels for atomization energies on QM9 using M3L correspond respectively to PW91, KT2, B97D, and τ-HCTH, B3LYP ∗ (VWN5), and TPSSH.

Unfolding of shocked hydrodynamic instability at SF[formula omitted] elliptical interface: Physical insights from numerical simulations

The Richtmyer–Meshkov (RM) instability widely exists in variety of industrial and scientific applications, such as in inertial confinement fusion, astrophysics explosions, supersonic combustion, bubble dynamics and cavitation. This study investigates the unfolding physical phenomena of RM instability at SF6 elliptical bubbles using numerical simulations. In contrast to cylindrical bubble, both shocked horizontal/vertical-aligned elliptical bubbles with different aspect ratios are considered, emphasizing the aspect ratio effects on flow morphology, complex wave patterns, interface deformation, vorticity generation, and interface features. For this purpose, a two-dimensional system of compressible Navier–Stokes-Fourier equations for multicomponent flows are solved using by a mixed modal discontinuous Galerkin method. Numerical simulations illustrate that the aspect ratio of elliptical bubbles have a significant influence on the vortex dynamics and the associated RM instability flows in contrast to the cylindrical bubble. In horizontal-aligned elliptical bubbles, some additional complex waves pattern, including inward jet and secondary vortex rings are observed. While, in vertical-aligned elliptical bubbles, the interface between two primary vortex rings is always vertical and has a “hippocampus” appearance due to the enhancement of downstream velocity. The baroclinic vorticity due to misalignment of density and pressure gradients at interface affects the bubble deformation and induces the Kelvin–Helmholtz instability, which leads to turbulent mixing. It is observed that the complexity of the vorticity fields is enhanced with increasing aspect ratios. Further, these aspect ratio effects result in integral diagnostic of important spatially integrated fields, and various interface features.

Assessing eDNA capture method from aquatic environment to optimise recovery of human mt-eDNA

Previous studies have shown that environmental DNA (eDNA) from human sources can be recovered from natural bodies of water, and the generation of DNA profiles from such environmental samples may assist in forensic investigations. However, fundamental knowledge gaps exist around the factors influencing the probability of detecting human eDNA and the design of optimal sampling protocols. One of these is understanding the particle sizes eDNA signals are most strongly associated with and the most appropriate filter size needed for efficiently capturing eDNA particles. This study assessed the amount of mitochondrial eDNA associated with different particle sizes from human blood and skin cells recovered from freshwater samples. Samples (300 mL) were taken from experimental 10 L tanks of freshwater spiked with 50 µL of human blood or skin cells deposited by vigorously rubbing hands together for two minutes in freshwater. Subsamples were collected by passing 250 mL of experimental water sample through six different filter pore sizes (from 0.1 to 8 µm). This process was repeated at four time intervals after spiking over 72 hours to assess if the particle size of the amount of eDNA recovered changes as the eDNA degrades. Using a human-specific quantitative polymerase chain reaction (qPCR) assay targeting the HV1 mitochondrial gene region, the total amount of mitochondrial eDNA associated with different particle size fractions was determined. In the case of human blood, at 0 h, the 0.45 µm filter pore size captured the greatest amount of mitochondrial eDNA, capturing 42 % of the eDNA detected. The pattern then changed after 48 h, with the 5 µm filter pore size capturing the greatest amount of eDNA (67 %), and 81 % of eDNA at 72 h. Notably, a ten-fold dilution proved to be a valuable strategy for enhancing eDNA recovery from the 8 µm filter at all time points, primarily due to the PCR inhibition observed in hemoglobin. For human skin cells, the greatest amounts of eDNA were recovered from the 8 µm filter pore size and were consistent through time (capturing 37 %, 56 %, and 88 % of eDNA at 0 hours, 48 hours, and 72 hours respectively). There is a clear variation in the amount of eDNA recovered between different cell types, and in some forensic scenarios, there is likely to be a mix of cell types present. These results suggest it would be best to use a 5 µm filter pore size to capture human blood and an 8 µm filter pore size to capture human skin cells to maximize DNA recovery from freshwater samples. Depending on the cell type contributing to the eDNA, a combination of different filter pore sizes may be employed to optimize the recovery of human DNA from water samples. This study provides the groundwork for optimizing a strategy for the efficient recovery of human eDNA from aquatic environments, paving the way for its broader application in forensic and environmental sciences.

TikTok for learning through movement, dramatization, and music: Insights for higher education applications in sport sciences

Social media has emerged as one of the most transformative digital phenomena that has reshaped education over the past two decades. New generations of students, often referred to as digital natives, increasingly utilise these platforms for socialization and interaction with their surrounding world. The pandemic prompted by the SARS-CoV-2 virus has further accelerated the adoption of online and hybrid teaching methodologies, wherein social media can assume a central role in creating dynamic teaching-learning environments. While the existing literature has highlighted the advantageous aspects of social media within the educational sphere, the landscape constantly evolves, witnessing the ongoing integration of new functionalities into existing platforms or the emergence of novel social media. TikTok serves as a prime illustration of this phenomenon, a social media platform centered on short videos complemented by music and its effects. Despite TikTok's burgeoning global user base, especially among younger people, its potential in the realm of higher education remains relatively unexplored. This pedagogical paper aims to address this gap in the literature by elucidating an experience involving TikTok within the context of sport sciences. Furthermore, it provides a series of guidelines and recommendations for university professors to incorporate TikTok into their instructional practices and delineates the potential applications of TikTok in various other contexts. These insights may serve as stepping stones for future investigation and educational undertakings.

Implementation science and psycho-oncology: Advancing the translation of evidence into practice.

This Special issue of Psycho-Oncology highlights examples of the application of implementation science to research in psycho-oncology. The aim is to demonstrate the different ways that implementation science is being used to generate evidence that can more readily translate evidence into changes in clinical practice. We hope this issue fosters greater interest in using the tools of implementation science to improve the lives of people affected by cancer. The papers in the issue were selected from among those that responded to a call for submissions on the application of implementation science frameworks and methods to issues in psycho-oncology. The focus included but was not limited to research on: understanding barriers and facilitators of intervention/practice adoption; assessing implementation outcomes, evaluating implementation strategies, and improving behavioural and/or clinical outcomes. The 11 papers in this issue were grouped for presentation purposes into four common topics: barriers and facilitators to implementation; feasibility as a key implementation outcome; the design, selection and adaptation of implementation strategies; and building the foundation for psycho-oncology research translation via systematic reviews that focus on implementation strategy design. These papers demonstrate the breadth of current applications of implementation science to research in psycho-oncology. Alongside the studies featured in this issue, including cost-effectiveness analyses, tests of nationally-focused strategies and proactive planning for adaptation, we look forward to other innovations that will promote further growth of both disciplines to improve the integration of psycho-oncology interventions across healthcare systems.

Exploring the Targets and Molecular Mechanisms of Curcumin for the Treatment of Bladder Cancer Based on Network Pharmacology, Molecular Docking and Molecular Dynamics.

Curcumin, a phenolic compound derived from turmeric, has demonstrated anti-tumor properties in preclinical models of various cancers. However, the exact mechanism of curcumin in treating bladder cancer remains unclear. This study aimed to elucidate the therapeutic targets and molecular mechanisms of curcumin in the treatment of BC through an integrated approach of network pharmacology, molecular docking, and molecular dynamics simulations. PharmMapper, SuperPred, TargetNet, and SwissTargetPrediction were utilized to acquire targets associated with curcumin, while GeneCards, CTD, DisGeNET, OMIM, and PharmGKB databases were utilized to obtain targets related to bladder cancer. The drug-disease interaction targets were obtained using Venny 2.1.0, and GO and KEGG enrichment analyses were then conducted with the DAVID tool. We constructed a protein-protein interaction (PPI) network and identified tenkey targets. In conclusion, AutoDock Tools 1.5.7 was utilized to conduct molecular docking simulations, followed by additional analysis of the central targets through the GEPIA, HPA, cBioPortal, and TIMER databases. A total of 305 potential anticancer targets of curcumin were obtained. The analysis of GO functional enrichment resulted in a total of 1105 terms, including 786 terms related to biological processes (BP), 105 terms related to cellular components (CC), and 214 terms related to molecular functions (MF). In addition, KEGG pathway enrichment analysis identified 170 relevant signaling pathways. Treating bladder cancer could potentially involve inhibiting pathways like the PI3K-Akt signaling pathway, MAPK signaling pathway, EGFR tyrosine kinase inhibitor resistance, and IL-17 signaling pathway. Activating TNF, ALB, CASP3, and ESR1 while inhibiting AKT1, EGFR, STAT3, BCL2, SRC, and HSP90AA1 can also hinder the proliferation of bladder tumor cells. According to the results of molecular docking, curcumin binds to these central targets in a spontaneous manner, exhibiting binding energies lower than -1.631kJ/mol. These findings were further validated at the transcriptional, translational and immune infiltration levels. By utilizing network pharmacology and molecular docking techniques, it was discovered that curcumin possesses diverse effects on multiple targets and pathways for treating bladder cancer. It has the potential to impede the growth of bladder tumor cells by suppressing various pathways including the PI3K-Akt and MAPK signaling pathways, as well as pathways associated with EGFR tyrosine kinase inhibitor resistance and the IL-17 signaling pathway. Curcumin could potentially disrupt the cell cycle advancement in bladder cancer cells by increasing the expression of TNF, ALB, CASP3, and ESR1 while decreasing AKT1, EGFR, STAT3, BCL2, SRC, HSP90AA1, and other targeted genes. These findings reveal the possible molecular pathways through which curcumin exerts its anticancer effects in bladder cancer, and this novel research strategy not only provides an important basis for an in-depth understanding of the anticancer mechanism of curcumin, but also offers new potential drugs and targets for the clinical treatment of bladder cancer. Therefore, this study is of great scientific significance and practical application value for promoting the development of bladder cancer therapeutic field. This finding provides strong support for the development of novel, safe and effective drugs for bladder cancer treatment.

Micelle-mediated growth of gold nanocrystals on a surface

Understanding nanocrystal growth is crucial for several reasons, as it plays a significant role in various scientific, technological, and industrial applications. Knowledge of nanocrystal growth allows scientists and engineers to control the size, shape, and composition of nanocrystals. This control is essential for tailoring the properties of nanomaterials for specific applications, such as electronics, catalysis, sensing and medicine. Nanocrystals are used in various biomedical applications, such as drug delivery, imaging, etc. Understanding their growth is essential for designing nanomaterials with specific functionalities for targeted drug delivery and imaging modalities. A molecular dynamics simulation study has been carried out to investigate the growth of nanocrystals in aqueous solutions in the presence of surfactants. In this study, we investigated the structural and thermodynamic properties of a micelle formed by cetyltrimethylammonium bromide (CTAB) in the aqueous solution and used that to quantify its role in the nucleation process when gold nucleates are in close proximity to another gold nanoparticle or surface. The path through which the nucleate gets fused with other entities, such as gold nanoparticles or surfaces, in the presence of a micelle in solution needs an explanation. We have shown the path and quantified energy required for the gold nucleate to get infused into the gold surface. Well-dispersed sets of gold atoms in solution were subjected to a series of steered molecular dynamics simulations, and it revealed that gold atoms, individually, must overcome an energy barrier of 10.52 ± 0.3 kcal/mol to start nucleating on the substrate. Due to this energy barrier, atoms form aggregate or nucleate inside the micelle. To control the release of the engulfed nucleate into the micelle onto the substrate, one needs to understand the mechanism of its release and quantify the energy requirement for the process. Here, we have used an enhanced sampling technique (umbrella sampling) to calculate the free energy required for the process mentioned above. A gold nucleate would require an additional energy of ∼10.36 kcal/mol to break the strong layering of micelle and water on the substrate. The radius of gyration and order parameter calculations show the extent of deformation of the micelle while releasing the engulfed nucleate on the substrate, and it gives us a recipe to alter the shape of the micelle and easier release of the nucleate on the surface. This study summarizes the path of nucleation of nucleates in the micellar solution and the recipe for the easier release of nucleates on the substrate.

Scalable multiparty steering using a single entangled photon-pair

The distribution and verification of quantum nonlocality across a network of users is essential for future quantum information science and technology applications. However, beyond simple point-to-point protocols, existing methods struggle with increasingly complex state preparation for a growing number of parties. Here, we experimentally demonstrate multiparty detection-loophole-free quantum steering, where one party simultaneously steers multiple spatially separate parties, using a multi-qubit state produced from a set of qubits of which only one pair is entangled. We derive loss-tolerant steering inequalities, enabling our experiment to close the detection loophole, and enabling us to show the scalability of this approach to rigorously verify quantum nonlocality across arbitrarily many parties. This provides practical tools for developing the future quantum internet.

Role of Biotechnology and Genetic Engineering in Bioremediation of Cadmium Pollution

Cadmium (Cd) is ubiquitous and an unessential trace element existing in the environment. Anthropogenic activities and applications of synthetic phosphate fertilizers greatly enhance the concentration of Cadmium in the environment, which proves to be carcinogenic. The long-term effects of heavy metals contamination on plants and animals have recently become a major public health concern. Thanks to the application of science and technology, new environmental initiatives can have a lower environmental impact significantly. The role of microbes is very well known and must be considered as potential pollutant removers. Microbial flora can remove heavy metals and oil from contaminated soil and water. In comparison to conventional techniques, bioremediation itself proved to be a more potent technique because the established mechanisms render it ineffective. Biotechnological advancements are inherently harmful to the environment because they have the potential to reduce metal pollution. Pollutants in the environment can be effectively removed using bioremediation. Both native and introduced species can thrive in a microorganism-friendly environment.

Guided inquiry with Moodle to improve students’ science process skills and conceptual understanding

This study aims to improve and describe science process skills (SPS) and conceptual understanding (CU) college students through guided inquiry learning with Moodle (GI-Moodle). This quasi-experimental study used a pretest-posttest non-equivalent control group design. College students taking general biology courses at Faculty of Applied Science and Engineering (FSTT), Universitas Pendidikan Mandalika (UNDIKMA) participated in this study. They were divided into three classes: the experiment, control 1, and control 2. Their SPS was measured using an essay test instrument containing 18 items, while the CU was examined using 50 items multiple choice test and 5 items essay test. The obtained data were analyzed using the analysis of covariance (ANCOVA) test. The analysis results identified different average students’ SPS and CU before and after they attended the learning processes using GI-Moodle, guided inquiry with WhatsApp group (GI-WAG), and structured inquiry with WhatsApp group (SI-WAG). The experiment class attended learning using GI-Moodle presented a more significant increase of SPS and CU than the students attending the other two learning with GI-WAG and SI-WAG. Therefore, the GI with Moodle learning can be used to improve students’ SPS and CU during post COVID-19 pandemic. Besides, further studies are suggested to use a more number and broader participants and identify the influence of GI-Moodle on other variables.