Application Framework Research Articles

Innovative Professional Learning Communities and Sustainable Education Practices through Digital Transformation

Education is the most important part of sustainable society development. Traditional approaches to permanent lifelong educator training often fail to produce lasting changes in teaching practices (UN, Goal 4). This paper examines how educator involvement in a professional learning community (PLC) ensures equitable quality education and promotes lifelong learning opportunities for all and a sustainable digital transformation of the teaching and learning process. Participation in PLC and the use of contemporary learning environments positively impact the quality of educators’ work, thereby enhancing the overall educational process. This study utilised a quantitative methodology, employing both descriptive and inferential statistics to analyse the data. Results indicate that a balanced integration of formal PLC structures and guidance, combined with elements of a non-formal, broader PLC and external incentives and support (such as projects), can surpass mere exchanges of best practices, foster innovation, and support the sustainable digital transformation of educators. This approach leads to a sustainable shift in pedagogical paradigms. The PLC_IP model (focused on including digital technologies and effective teaching strategies) enhances collective educator efficacy, contributing to improved teaching quality. For PLCs to achieve sustainability, they must be dynamic entities that adapt to the evolving educational landscape, ensuring their continued relevance and impact with rising completion rates over time. Our findings underscore the importance of innovative PLCs in driving sustainable educational development and improvement and provide a framework for future research and practical applications in educational settings. In addition, PLCs can strengthen the means of implementation and revitalise the global partnership for sustainable development (UN, Goal 17).

Real-time object detection, tracking, and monitoring framework for security surveillance systems

The concept of security is becoming a global challenge, and governments, stakeholders, corporate societies, and individuals must urgently create a reasonable protection mechanism for good. Therefore, a real-time surveillance system is essential for detection, tracking, and monitoring. Many studies have attempted to provide better solutions but more research and better approaches are essential. This study presents a real-time framework for object detection and tracking for security surveillance systems. The system has been designed based on approximate median filtering, component labeling, background subtraction, and deep learning approaches. The new algorithms for object detection, tracking, and recognition have been implemented using Python and integrated with C# programming languages for ease of use. A software application framework is designed, implemented, and evaluated. The experimental results based on MOT-Challenge performance metrics show that the proposed algorithms have much better performance in terms of accuracy and precision on the MOT15, MOT16, and MOT17 datasets compared to state-of-the-art approaches. This framework also provides an accurate and effective means of monitoring and recognizing moving objects. The software development, including the design of the framework user interfaces, is coded in the C# programming language and integrated with Python using Microsoft Visual Studio (2019 edition). The integration is performed to provide a convenient user interface and to enable the execution of the framework as a standard and standalone software application. Future studies will consider the dynamic scalability of the framework to accommodate different surveillance application areas in overcrowded scenarios. Multiple data sources are integrated to enhance the performance for different scene times, locations, and weather conditions. Furthermore, other object-detection techniques such as You Only Look Once (YOLO) and its variants shall be considered in future studies. These techniques allow the framework to adapt to complex situations in which security surveillance is challenging.

Migration policies versus public health - the ethics of Covid-19 related movement restrictions for asylum seekers in reception centers in Greece in 2020.

The emergency context of the Covid-19 pandemic necessitated the use of national and international public health measures of unprecedented scale to minimize mortality and morbidity, often in conflict with other principles and rights, such as the autonomy of individuals. Concerns have been voiced that for populations facing precarity, such as migrants, a disproportionate and unfair application of restrictive measures, deficient application of protective measures, and even enforcement of restrictive migration policies under the pretext of the pandemic has occurred. Various principles have been proposed as moral foundations of public health interventions. The author used two public health ethics frameworks to examine the ethical acceptability of movement restrictions on asylum seekers residing in refugee camps in Greece from March 2020 to October 2020. Most of the principles described in the frameworks for the ethical application of movement restrictions were not adhered to. Main concerns include that, measures were prolonged despite lack of evidence about their effectiveness to reduce morbidity and mortality, while posing severe and disproportionate burdens for this population. An ethically acceptable public health response to Covid-19 is incompatible with certain living conditions of refugees, asylum seekers, and migrants. The question of whether and if so the extent to which the discipline of public health inherently has the role of rectifying existing injustices and social inequalities when these can be convincingly related to health outcomes, is central to the design of public health interventions for these populations. The answer can exemplify the need to address moral and political determinants of health. It is essential for public health professionals to be aware of the moral theorizations that underpin their work, so as to ensure that their policies are aligned with those and to contribute to the debate that shapes these determinants.

Plant-Based Shape Memory Cryogel for Hemorrhage Control.

The escalating global demand for sustainable manufacturing, motivated by concerns over energy conservation and carbon footprints, encounters challenges due to insufficient renewable materials and arduous fabrication procedures to fulfill specific requirements in medical and healthcare systems. Here, biosafe pollen cryogel is engineered as effective hemostats without additional harmful crosslinkers to treat deep noncompressible wounds. A straightforward and low-energy approach is involved in forming stable macroporous cryogel, benefiting from the unique micro-hierarchical structures and chemical components of non-allergenic plant pollen. It is demonstrated that the pollen cryogel exhibits rapid water/blood-triggered shape-memory properties within 2 s. Owing to their inherent nano/micro hierarchical structure and abundant chemical functional groups on the pollen surface, the pollen cryogel shows effective hemostatic performance in a mouse liver penetration model, which is easily removed after usage. Overall, the self-crosslinking pollen cryogel in this work pioneers a framework of potential clinical applications for the first-hand treatment on deep noncompressible wounds.

Transforming E-Commerce Logistics: Sustainable Practices through Autonomous Maritime and Last-Mile Transportation Solutions

The logistics landscape in e-commerce is undergoing a profound transformation toward sustainability and autonomy. This paper explores the implementation of autonomous maritime and last-mile transportation solutions to optimize the entire logistics chain from factory to customer. Building on the lessons learned from the maritime industry’s digital transformation, the study identifies key features and proposes a forward-looking autonomous maritime and last-mile transportation system. Emphasizing the role of geospatial technologies, the proposed system employs GIS-based electronic route optimization for efficient goods delivery, integrating onboard and ashore GIS-based sensors for enhanced location precision. A case study was built to analyze the implementation of autonomous means of transport along the route of a product from factory to customer. The integration of autonomous systems shows substantial improvements in logistics performance. Synchromodal logistics and smart steaming techniques can be utilized to optimize transportation routes, resulting in reduced fuel consumption and emissions. The findings reveal that autonomous maritime and last-mile transport systems can significantly enhance the efficiency, flexibility and sustainability of e-commerce logistics. The study emphasizes the need for advanced technological integration and provides a comprehensive framework for future research and practical applications in the logistics industry.

Beyond Belief and Practice: An Exploratory Literature Review and Discussion of the Differential Impact of Spirituality and Religiosity on Mental Health Disorders.

The relationship between spirituality and religiosity and their impact on mental health is intricate and underexplored. This exploratory review aims to elucidate the distinct effects of these constructs, highlighting their contributions to psychological well-being and clinical practices. By dissecting the impacts of spirituality and religiosity on mental health, the study focuses on their individual and combined roles in shaping therapeutic approaches and theoretical understandings in the field. A literature review was conducted using PubMed, focusing on articles discussing spirituality, religiosity, and their intersection with mental health and psychopathology. Out of 312 identified articles, 69 peer-reviewed articles were included after screening for relevance. The results indicate that spirituality and religiosity significantly influence mental health yet are often conflated, leading to research inconsistencies and clinical challenges. Spirituality, as a broad and individualistic pathway, enhances personal well-being and resilience, often transcending organized religious practices. In contrast, religiosity, with its structured community support, sometimes imposes constraints that exacerbate stress under specific doctrinal pressures. Neurobiological evidence suggests that both constructs interact with cognitive processes and brain function, influencing emotional regulation and stress response. The study concludes that distinguishing between spirituality and religiosity is essential for precise academic discourse and effective clinical practice. This differentiation allows for more personalized therapeutic approaches, accommodating an individual's spiritual and religious contexts. The authors propose a refined framework for future research and therapeutic applications to be sensitive to the nuanced experiences of individuals and to better tailor interventions in clinical settings.

Automation of ePROMs in radiation oncology and its impact on patient response and bias

PurposeThis study evaluates the impact of integrating a novel, in-house developed electronic Patient-Reported Outcome Measures (ePROMs) tool with a commercial Oncology Information System (OIS) on patient response rates and potential biases in real-world data science applications. Materials and MethodsWe designed an ePROMs tool using the NodeJS web application framework, automatically sending e-mail questionnaires to patients based on their treatment schedules in the OIS. The tool is used across various treatment sites to collect PROMs data in a real-world setting. This research examined the effects of increasing automation levels on both recruitment and response rates, as well as potential biases across different patient cohorts. Automation was implemented in three escalating levels, from telephone reminders for missing reports to minimal intervention from study nurses. ResultsFrom August 2020 to December 2023, 1,944 patients participated in the PROMs study. Our findings indicate that automating the workflows substantially reduced the patient management workload. However, higher levels of automation led to lower response rates, particularly in collecting late-phase symptoms in breast and head-and-neck cancer cohorts. Additionally, email-based PROMs introduced an age bias when recruiting new patients for the ePROMs study. Nevertheless, age was not a significant predictor of early dropout or missing symptom reports among patients participating. Notably, increased automation was significantly correlated with lower response rates in breast (p = 0.026) and head-and-neck cancer patients (p < 0.001). ConclusionIntegrating ePROMs within the OIS can significantly reduce workload and personnel resources. However, this efficiency may compromise patient responses in certain groups. A balance must be achieved between workload, resource allocation, and the sensitivity needed to detect clinically significant effects. This may necessitate customized automation levels tailored to specific cancer groups, highlighting a fundamental trade-off between operational efficiency and data quality.

Why the use of domain-specific modeling in airworthy software requires new methods and how these might look like? (extended version)

AbstractThe use of domain-specific modeling (DSM) in safety-critical avionics is rare, even though the ever-increasing complexity of avionics systems makes the use of DSM reasonable. DSM shows its advantage especially in capturing complex systems, data and relationships. The reason for the limited use in the (safety-critical) avionics area is mainly due to the high demands on the safety of software and systems. Everything that is to be used in flight operations and development must undergo a rigorous and complex certification process. Any data used in operations must be verified. A reduction of this effort can be achieved using qualified tools. A qualified tool can either replace or support certification activities. This article elaborates different use cases of how DSM could be used in relation to airworthy software. For those use cases, we review the effort of a certification and retrieve the major shortcomings and showstoppers of available frameworks, e.g. infeasible qualification of DSM runtimes and the unavailability of qualification artifacts. Finally, we elaborate possible ways of mitigation and show the concept and first results of a new DSM framework for airworthy applications, called DOMAINES. DOMAINES covers deterministic meta-modeling up to graph-based model transformations and verified visual editing. The concept of DOMAINES and a first functional prototype are presented that indicate that the shortcomings can be mitigated.

Proposed cancelable biometrics system based on hybrid optical crypto-steganography audio framework for cyber security applications

Proposed cancelable biometrics system based on hybrid optical crypto-steganography audio framework for cyber security applications

Possible detection of atmospheric bioaerosol via LiDAR: a wavelength-based simulation study

This study explores potential of LiDAR technology to rapidly detect aerosolized biological terror agents in the atmosphere. It assesses the application by simulating extinction coefficients and the Ångström exponent at various wavelengths (266, 1064, 1571, and 2000 nm), focusing on differentiating bioaerosols from typical atmospheric particles. The simulation analysis evaluates changes in aerosol distributions and related extinction coefficient and Ångström exponent shifts under clean, normal, and bad atmospheric conditions. The findings indicate that the 1064 nm wavelength effectively detects bioaerosol presence, with a combination of 1064 nm and 1571 nm providing optimal Ångström exponent use for particle size differentiation. This dual-wavelength approach is highlighted as a practical method for bioaerosol detection, showcasing a significant sensitivity to variations in particle quantity and size, which are critical in biological threat scenarios. In conclusion, the study offers guidance for selecting LiDAR wavelengths for biological agent detection systems. While providing a theoretical framework for practical applications, it also underlines the need for further experimental work to confirm findings and fine-tune technology for real-world monitoring and threat management. This research contributes to the development of effective monitoring strategies against the backdrop of biological terror threats.Graphical

Large-Scale Indoor Camera Positioning Using Fiducial Markers.

Estimating the pose of a large set of fixed indoor cameras is a requirement for certain applications in augmented reality, autonomous navigation, video surveillance, and logistics. However, accurately mapping the positions of these cameras remains an unsolved problem. While providing partial solutions, existing alternatives are limited by their dependence on distinct environmental features, the requirement for large overlapping camera views, and specific conditions. This paper introduces a novel approach to estimating the pose of a large set of cameras using a small subset of fiducial markers printed on regular pieces of paper. By placing the markers in areas visible to multiple cameras, we can obtain an initial estimation of the pair-wise spatial relationship between them. The markers can be moved throughout the environment to obtain the relationship between all cameras, thus creating a graph connecting all cameras. In the final step, our method performs a full optimization, minimizing the reprojection errors of the observed markers and enforcing physical constraints, such as camera and marker coplanarity and control points. We validated our approach using novel artificial and real datasets with varying levels of complexity. Our experiments demonstrated superior performance over existing state-of-the-art techniques and increased effectiveness in real-world applications. Accompanying this paper, we provide the research community with access to our code, tutorials, and an application framework to support the deployment of our methodology.

Estimating the axial strain of circular short columns confined with CFRP under centric compressive static load using ANN and GRA techniques

This investigation introduces advanced predictive models for estimating axial strains in Carbon Fiber-Reinforced Polymer (CFRP) confined concrete cylinders, addressing critical aspects of structural integrity in seismic environments. By synthesizing insights from a substantial dataset comprising 708 experimental observations, we harness the power of Artificial Neural Networks (ANNs) and General Regression Analysis (GRA) to refine predictive accuracy and reliability. The enhanced models developed through this research demonstrate superior performance, evidenced by an impressive R-squared value of 0.85 and a Root Mean Square Error (RMSE) of 1.42, and significantly advance our understanding of the behavior of CFRP-confined structures under load. Detailed comparisons with existing predictive models reveal our approaches' superior capacity to mimic and forecast axial strain behaviors accurately, offering essential benefits for designing and reinforcing concrete structures in earthquake-prone areas. This investigation sets a new benchmark in the field through meticulous analysis and innovative modeling, providing a robust framework for future engineering applications and research.

Glossary of terms used in biochar research (IUPAC Technical Report)

Abstract Biochar is the solid carbonaceous product of biomass pyrolysis. The properties of biochar depend on the biomass feedstock as well as the pyrolysis temperature and time. Therefore, biochars with different properties and functionalities can be produced. Biochar research has been intensive in the past 15 years, focusing mainly on soil applications, wastewater treatment, and contaminant remediation. However, a formal definition of biochar and related terms is missing, which hinders the standardization of scientific results worldwide and the scaling-up of research at the industrial level. Furthermore, an official terminology may promote the development of a harmonized legal framework for biochar production and applications, both at regional and national levels. This glossary of terms consists of 178 scientifically sound definitions of the most commonly used terms in biochar research. The definitions of this glossary are interconnected, allowing the reader to further explore the synergies between terms. The distribution of terms reflects the multidisciplinarity of biochar research: chemistry, material science and engineering, and soil science are the main disciplines represented here. The list of terms is by no means exhaustive and the strategic objective of this effort is to develop a dynamic document in which more terms will be added in the future, and the existing ones will be refined, as biochar research evolves.

Modulation of mechanical properties of low molecular weight supramolecular hydrogels of calcium cholate by drugs of varying hydrophobicity: Effect on drug release, antioxidant, and antibacterial properties

Bile salt based low-molecular-weight supramolecular hydrogels (LMWSH) serve as an appealing framework for drug delivery applications, owing to their natural origin, biocompatibility, and the peculiar distribution of hydrophobic and hydrophilic domains. In this work, LMWSH based on an ionotropic gel of sodium cholate assisted by Ca2+ ions (CaC) was developed as a drug delivery system for both hydrophilic and hydrophobic drugs. Diosgenin, Quercetin, Rutin, and Amygdalin were chosen as model drugs, having varying hydrophobicity. The mechanical strength of the CaC hydrogel is enhanced by increasing the hydrophobicity of the loaded drug. The close-knit mesh design of CaC hydrogels loaded with hydrophobic drugs resulted in controlled drug release compared to the hydrophilic drugs. Drug-loaded CaC hydrogels exhibited good swelling and thixotropy, making them suitable for injectable formulations. In kinetic drug release, both relaxation and diffusion mechanisms play a pivotal role. The antioxidant and antibacterial activity of the loaded drugs increased synergistically in the hydrogels. Thus, due to these properties, the hydrogels can be safely used as topical gels against wound healing. This study shows that bile salts exhibit potential to enhance the delivery of both hydrophobic and hydrophilic drugs, making them promising candidates for biomedical applications.

EXTENDING SYSTEMS ENGINEERING FOR SAFETY‐CRITICAL DEFENCE APPLICATIONS

AbstractDefence sector applications are often characterised by a high level of complexity: in the technical systems involved, in their management and supply chain arrangements, and in the dynamic nature of the risks involved. ISO/IEC/IEEE 15288 is well established as a standard that provides a common set of life cycle processes and terminology for engineering complex systems. However, it takes a generic approach that does not directly address the needs of systems with a safety impact. In contrast, safety‐specific standards like IEC 61508 provide a well‐known framework for the functional safety of electrical, electronic, and programmable electronic safety‐related systems, but do not address the complexity commonly found in systems in the defence sector.In IEC 63187‐1, the International Electrotechnical Commission is drafting a new standard to provide a safety framework for defence applications. It uses modern systems engineering principles that build on ISO/IEC/IEEE 15288, extending it with requirements to make it appropriate for critical systems in the defence sector. This paper discusses how IEC 63187‐1 uses ISO/IEC/IEEE 15288 to achieve the goals of system safety, why this approach was adopted, the expected benefits and some of the impacts of designing the new safety framework this way.

Development of a Web-Based Interactive Tool for Visualizing Breast Cancer Clinical Trial Tolerability Data.

Longitudinal patient tolerability data collected as part of randomized controlled trials are often summarized in a way that loses information and does not capture the treatment experience. To address this, we developed an interactive web application to empower clinicians and researchers to explore and visualize patient tolerability data. We used adverse event (AE) data (Common Terminology Criteria for Adverse Events) and patient-reported outcomes (PROs) from the NSABP-B35 phase III clinical trial, which compared anastrozole with tamoxifen for breast cancer-free survival, to demonstrate the tools. An interactive web application was developed using R and the Shiny web application framework that generates Sankey diagrams to visualize AEs and PROs using four tools: AE Explorer, PRO Explorer, Cohort Explorer, and Custom Explorer. To illustrate how users can use the interactive tool, examples for each of the four applications are presented using data from the NSABP-B35 phase III trial and the NSABP-B30 trial for the Custom Explorer. In the AE and PRO explorers, users can select AEs or PROs to visualize within specified time periods and compare across treatments. In the cohort explorer, users can select a subset of patients with a specific symptom, severity, and treatment received to visualize the trajectory over time within a specified time interval. With the custom explorer, users can upload and visualize structured longitudinal toxicity and tolerability data. We have created an interactive web application and tool for clinicians and researchers to explore and visualize clinical trial tolerability data. This adaptable tool can be extended for other clinical trial data visualization and incorporated into future patient-clinician interactions regarding treatment decisions.

DEVELOPMENT OF AN APPLICATION FOR ASSESSING PUBLIC RADIATION EXPOSURE WHEN LIVING AND CARRYING OUT ANY ACTIVITIES AT THE SEMIPALATINSK TEST SITE

The article presents an overview of capabilities of developed software ‘Assessment of public radiation exposure when living and carrying out any activities at the Semipalatinsk Test Site’ and quoted the algorithm to calculate public individual annual effective exposure doses from the major dose-forming man-made radionuclides at the test site (137Cs, 90Sr, 239+240Pu, 241Am, 3H). The software developed takes into account such behavior scenario as a farmer engaged in subsistence farming and living in the study area together with his family members. The software was developed for Windows operating system and implemented in the development framework of cross-platform applications Embarcadero Delphi XE7 Professional in the object-oriented programming language Object Pascal.

Archery Analytic Workflow in a Web-Based Application

The integration of sports science and camera sensing technology has recently emerged to be an advanced analytical tool in sportsperson performance enhancement. The use of computing power and a web-based application can provide quick information analysis and data reporting between coaches and athletes. The design of an archery analytic workflow is demonstrated in this paper using the Python Flask framework, video analytic algorithms, a structured video inventory framework, MongoDB database setup and integration of the Keypoint R-CNN machine learning backend. A user-friendly data visualisation interface on the front end is integrated in the software to deliver athletes’ analytical capabilities such as thorough frame-by-frame video analysis, posture consistency estimation and joint kinematic analysis. This web application framework is not limited to archery sports, and can be extended to numerous sports, such as shooting, weightlifting and cycling. The significance of integrating camera sensing technology with the sports science field can offer quantitative and qualitative observations to improve training programs and performance evaluation.

Wavelength tunable noise-like pulses in a hybrid mode-locked erbium-doped fiber laser

The study demonstrates a new type of laser that generates wavelength-tunable h-shaped noise-like pulses (NLPs). We incorporated the nonlinear polarization rotation (NPR) mechanism into a figure-eight erbium-doped hybrid mode-locked fiber laser based on the nonlinear amplifier loop mirror (NALM). Under an intra-cavity net dispersion of −5.535 ps2, the system produced NLPs with a maximum pulse duration of 24.54 ns, a maximum single energy of 19.96 nJ and a maximum output average power of 14.46 mW when the pump power was set to 543 mW. By adjusting the pump power and fine-tuning the polarization controller, the laser can output NLPs with tunable wavelengths ranging from 1560 to 1601 nm. The flexibility of wavelength-tunable techniques has potential applications in laser detection, high-energy physics experiments, and other fields, enriching the research framework of noise-like applications.

Augmented Reality in Elementary Education: System Architecture for Implementing an Interactive and Immersive E-Learning Application

Early childhood language acquisition is crucial, but complex scripts such as Bengali present challenges. BIPLOB, an Android-based augmented reality (AR) application, seeks to revolutionize language learning by introducing interactive 3D models and meticulously recorded audio pronunciations. Targeting both engagement and accuracy, BIPLOB addresses issues encountered by traditional methods. Its optimized workflow offers a replicable framework for future AR-based language learning applications, especially for complex scripts. BIPLOB paves the way for immersive and effective early language acquisition through the transformative power of AR technology.