To cope with the complexity, the digital transformation of cyber-physical and socio-technology systems demands the utilization of heterogeneous tailorable development environments with dynamic configuring ability and transparent integration of independently developed dedicated frameworks. Hyper-frameworks, aka Framework-of-Frameworks, correspond to the System-of-Systems paradigm and designate a software-supported, meta-model-based generator of executable configurations. Armed with a meta-modeling layer, the incremental development of hybrid architecture instances focuses on language-based models and their transformations into functional, interpretable environments. Data-driven reasoning and decision support for the entire life cycle of digitally transformed systems critically depends on the effectiveness of involved generative mechanisms and the trustworthiness of related information resources. The Architecture, Meta-Configuration, and Information Resource Handling are the essential segments of the initial version of the proposed evolution prototype. In this article, we formulate five essential hypotheses founding the proposed digital transformation hyper-framework project that integrates the framework model with a meta-configuration-based generative mechanism. The Life Cycle Modeling component framework creatively integrates System, Software, and Operation Engineering. Specification and development of the additional component frameworks, in compliance with specified generative mechanisms, direct further refinements of the proposed Hyper-framework.

This paper deals with developing a new class of quaternions, called hyper-dual Horadam quaternions which are constructed from the quaternions whose components are hyper-dual Horadam numbers. We investigate the algebraic properties of these quaternions.

We examine the extensions of the basic arithmetical operations of addition and multiplication on the natural numbers into higher-rank hyper-operations also on the natural numbers. We go on to define the concepts of prime and composite numbers under these hyper-operations and derive some results about factorisation, resulting in fundamental theorems analogous to the Fundamental Theorem of Arithmetic.

Elevated immunoglobulin E (IgE) levels are commonly associated with allergies. However, high IgE levels are also found in several other infectious and non-infectious disorders. Elevated IgE levels typically suggest allergies, eczema, or recurrent skin infections. Very high IgE levels are uncommon in patients with active eczema but typically reflect a monogenic atopic condition or inborn immune defects with an atopic phenotype. The aim of our research is to identify if the possible cause of high IgE levels is also dependent on external factors, age, or associated recurrent pathologies such as infections. To answer this question, we performed a one-year retrospective study that included 200 hospitalized children who had at least two viral or bacterial infections in the six months preceding hospitalization. Measurements of IgE and allergen panels (respiratory and digestive) using blood samples revealed that individuals who tested positive for the body’s synthesis of hyperimmunoglobulin E were not observably allergic to any potential allergens despite having higher total serum IgE. According to the results, there was a strong correlation between elevated IgE serum levels and a history of infectious diseases among the patients.

The proper hydrogenation of Hyper-coal (HPC) using 1, 2, 3, 4-tetrahydroquinoline (THQ) was able to decrease the oxygen content and adjust the molecular structure of HPC for preparing the spinnable pitch with high softening point (SP). The spinnable pitch prepared from the THQ-soluble (QS) fraction of HPC as a precursor consisted more naphthenic carbon groups than that prepared from the 1-methylnaphthalene (1-MN) soluble (MNS) fraction of HPC. The HPC-QS derived pitch showed excellent spinnability even the SP of 260°C, and the tensile strength of the resultant carbon fiber was up to 1350 MPa with a diameter around 8 µm by only heat treatment at 800°C for 5 min.

The spectrums of one type of object under different conditions have the same features (up, down, protruding, concave) at the same spectral positions, which can be used as primary parameters to evaluate the difference among remotely sensed pixels. Wavelet-feature correlation ratio Markov clustering algorithm (WFCRMCA) for the remotely sensed data is proposed based on an accurate description of abrupt spectral features and an optimized Markov clustering in the wavelet feather space. The peak points can be captured and identified by applying a wavelet transform to spectral data. The correlation ratio between two samples is a statistical calculation of the matched peak point positions on the wavelet feature within an adjustable spectrum domain or a range of wavelet scales. The evenly sampled data can be used to create class centers, depending on the correlation ratio threshold at each Markov step, accelerating the clustering speed by avoiding the computation of Euclidean distance for traditional clustering algorithms, such as K-means and ISODATA. Markov clustering applies several strategies, such as a simulated annealing method and gradually shrinking the clustering size, to control the clustering convergence. At each clustering tempera-ture, it can obtain the best class centers quickly. The experimental results of the Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS) and Thermal Mapping (TM) data have verified its ac-ceptable clustering accuracy and high convergence velocity.

Gastrointestinal (GI) tract disorders, ranging from benign polyps to aggressive forms of cancer, pose significant health challenges globally. Early detection and precise classification of these conditions are crucial for effective treatment and improving patient survival rates. This study employs the Hyper-Kvasir dataset, a comprehensive collection of endoscopic images, to develop deep learning models that harness the power of the YOLO (You Only Look Once) architecture for real-time detection and classification of GI abnormalities. The focus is on overcoming inherent challenges such as class imbalance and limited annotated data availability. Advanced machine learning strategies, including data augmentation and semi-supervised learning, are utilized to enhance the model's performance. Our experiments demonstrate notable improvements in the detection of pre-cancerous lesions and other GI abnormalities, confirming the potential of integrating AI into endoscopic practices to support clinicians, reduce diagnostic errors, and contribute to more accurate and timely diagnoses. The implications of these findings are significant, offering a pathway to more reliable diagnostic processes and ultimately, better patient management in gastroenterology.

The first hyperpolarizability of graphene quantum dots (GQDs) suspended in water was determined using the hyper-Rayleigh scattering (HRS) technique. To the best of our knowledge, this is the first application of the HRS technique to characterize GQDs. Two commercial GQDs (Acqua-Cyan and Acqua-Green) with different compositions were studied. The HRS experiments were performed with an excitation laser at 1064 nm. The measured hyperpolarizabilities were (1.0±0.1) × 10^(-27) esu and (0.9±0.1) × 10^(-27) esu for Acqua-Cyan and Acqua-Green, respectively. The results were used to estimate the hyperpolarizability per nanosheet obtained by assuming that each GQDs has five nanosheets with 0.3 nm thickness. The two-level model, used to calculate the static hyperpolarizability per nanosheet, provides the values of (2.4±0.1) × 10^(-28) esu (Acqua-Cyan) and (0.5±0.1) × 10^(-28) esu (Aqua-Green). The origin of the nonlinearity is discussed on the basis of polarized resolved HRS experiments, and an electric quadrupolar behavior with a strong dependence on surface effects is found. The nontoxic characteristics and order of magnitude indicate that these GQDs may be useful for biological microscopy imaging.

We have particularly investigated the correlation law of the effect of different carbon black fillings on the hyper-elastic mechanical behavior of natural rubber by conducting uniaxial tensile tests over a wide range of deformations with different volume fractions of carbon black fillings (0%, 4.7%, 8.9%, 12.8%, 16.4%, 19.7%, 22.7%, 25.2%). The results show that the stress-strain curve for carbon black filled rubber increases with the amount of filling, meaning that the rubber gradually becomes "harder". We explore the correlation between the carbon black filling volume and the parameters of the Yeoh constitutive model by examining the Yeoh constitutive model to characterize the hyper-elastic mechanical behavior of rubber with different carbon black fillings. A quantitative relationship between the material parameters and the carbon black filling volume in the Yeoh constitutive model is presented. A method for calculating the material parameters of the Yeoh constitutive model is developed and it predicts the correlation between the hyper-elastic properties of rubber and the volume fraction of carbon black filling.

A wealth of Full Waveform (FW) LiDAR data are available to the public from different sources, which is poised to boost the extensive application of FW LiDAR data. However, we lack a handy and open source tool that can be used by potential users for processing and analyzing FW LiDAR data. To this end, we introduce waveformlidar, an R package dedicated to FW LiDAR processing, analysis and visualization as a solution to the constraint. Specifically, this package provides several commonly used waveform processing methods such as Gaussian, adaptive Gaussian and Weibull decompositions, and deconvolution approaches (Gold and Richard-Lucy (RL)) with users’ customized settings. In addition, we also developed functions to derive commonly used waveform metrics for characterizing vegetation structure. Moreover, a new way to directly visualize FW LiDAR data is developed through converting waveforms into points to form the Hyper Point cloud (HPC), which can be easily adopted and subsequently analyzed with existing discrete-return LiDAR processing tools such as LAStools and FUSION. Basic explorations of the HPC such as 3D voxelization of the HPC and conversion from original waveforms to composite waveforms are also available in this package. All of these functions are developed based on small-footprint FW LiDAR data, but they can be easily transplanted to the large footprint FW LiDAR data such as Geoscience Laser Altimeter System (GLAS) and Global Ecosystem Dynamics Investigation (GEDI) data analysis. It is anticipated that these functions will facilitate the widespread use of FW LiDAR and be beneficial for better estimating biomass and characterizing vegetation structure at various scales. The package and code examples can be found at https://github.com/tankwin08/waveformlidar.

The hyper-arid environment is characterized by high inter-annual climatic fluctuations. The yearly average rainfall can change substantially for several years, forming wet or dry sub-periods. Observing rainfall trends over a sub-period can lead to a false perception of a change in the trend but may in fact represent a periodic cycle when examined on a larger time scale. We aimed to better characterize the rainfall regime prevailing in the hyper-arid Arava Valley (Israel/Jordan), and to examine the response of vegetation to annual rainfall. We hypothesized that annual and perennial vegetation would respond differently to wet and dry sub-periods, and that grazing activities will impact vegetation growth. We used a time series of monthly rainfall, from which we calculated Standard Precipitation Index (SPI), and calculated proxies of perennial and annual vegetation over the last four decades using Landsat-derived Normalized Difference Vegetation Index (NDVI). We found no clear trend in rainfall amounts during this period, however we did identify wet and dry sub-periods which were statistically distinct in rain and in vegetation patterns from each other. The highest levels of correlation between rainfall and the NDVI derived proxies were found when examining average rainfall over a period of two- three years for the annual vegetation and over four years for perennial vegetation. Using the Mann-Kendall test, we identified a time lag of two to four years, with the proxies of annual vegetation responding faster than the proxies of perennial vegetation, to shifts between wet and dry sub-period. In addition, we found a consistent difference between natural vegetation cover in Jordan (grazed) and Israel (non-grazed), favoring the development of natural vegetation on the Israeli side. We conclude that integrating between long-term remote sensing satellite imagery and climatic records revealed the greater resilience of perennial vegetation in the hyper-arid region to climatic fluctuation, and enabled us to identify the vegetation’s sensitivity to anthropogenic impact.

The expansion of copper mining on the hyper-arid pacific slope of southern Peru has precipitated growing concern for scarce water resources in the region. Located in the headwaters of the Torata river, in the department of Moquegua, the Cuajone mine, owned by Southern Copper, provides a unique opportunity, in a little-studied region, to examine the relative impact of the landscape-scale mining on water resources in the region. Principal component analysis and cluster analysis of the water chemistry data from 16 sites, collected over three seasons during 2017 and 2018, show distinct statistical groupings indicating that, above the settlement of Torata, water geochemistry is a function of chemical weathering processes acting upon underlying geological units, and confirming that the Cuajone mine does not significantly affect water quality in the Torata river. Impact mitigation strategies that firstly divert channel flow around the mine and secondly divert mine waste to the Toquepala river and tailings dam at Quebrada Honda, below the Toquepala mine, in the department of Tacna, secure water quality in the Torata river for the foreseeable future. In the greater Moquegua river catchment, our results further suggest that water quality has been more significantly impacted by urban effluents and agricultural runoff than the Cuajone mine. The increase in total dissolved solids in the waters of the lower catchment reflects the cumulative addition of dissolved ions through chemical weathering of the underlying geological units, supplemented by rapid recharge of surface waters contaminated by residues associated with agricultural and urban runoff through the porous alluvial aquifer. Concentrations in some of the major ions exceeded internationally recommended maxima for agricultural use, especially in the coastal region. Occasionally, arsenic and manganese contamination also reached unsafe levels for domestic consumption. In the lower catchment, below the Cuajone mine, data and multivariate analyses point to urban effluents and agricultural run-off rather than weathering of exposed rock units, natural or otherwise, as the main cause of contamination.

This paper describes and demonstrates an autonomous robotic team that can rapidly learn the characteristics of environments that it has never seen before. The flexible paradigm is easily scalable to multi-robot, multi-sensor autonomous teams, and is relevant to satellite calibration/validation and the creation of new remote sensing data products. A case study is described for the rapid characterisation of the aquatic environment, over a period of just a few minutes we acquired thousands of training data points. This training data allowed our machine learning algorithms to rapidly learn by example and provide wide area maps of the composition of the environment. Along side these larger autonomous robots two smaller robots that can be deployed by a single individual were also deployed (a walking robot and a robotic hover-board), observing significant small scale spatial variability.

Hypergravity accelerators are a type of large machinery used for gravity training or medical research. A failure of such large equipment can be a serious problem in terms of safety or costs. This paper proposes a prediction model that can proactively prevent failures that may occur in a hy-pergravity accelerator. The method proposed in this paper was to convert vibration signals to spectograms and perform classification training using a deep learning model. An experiment was conducted to evaluate the performance of the method proposed in this paper. A 4-channel accel-erometer was attached to the bearing housing, which is a rotor, and time-amplitude data were obtained from the measured values by sampling. The data were converted to a two-dimensional spectrogram, and classification training was performed using a deep learning model for four conditions of the equipment: Unbalance, Misalignment, Shaft Rubbing, and Normal. The ex-perimental results showed that the proposed method had a 99.5% F1-Score, which was up to 23% higher than the 76.25% for existing feature-based learning models.

Water reuse is critical to national development, sustenance, and survival in this era of climate, demographic, and social changes. There is no national systemic approach to address the challenge systematically. The paper presents a framework and a method to develop a national research strategy for water reuse. It presents an ontology of water reuse strategy that encapsulates the combinatorial complexity of the problem clearly, concisely, and comprehensively. Subsequently, it discusses the method to use the framework to develop a national strategy, adapt it through feedback and learning, and ultimately effect a revolutionary change in the strategy for water reuse.

Fire hazards present a multifaceted global challenge that endangers human lives and environmental safety. Despite advancements in fire risk management technologies and methods, fires continue to be a prime concern for human security. The Arabian Gulf region, influenced by its climatic and environmental conditions, is especially susceptible to these threats. The Emirate of Sharjah, part of the United Arab Emirates, has made significant progress in reinforcing fire safety measures within residential structures, achieving a decrease in the number of fire-related accidents compared to neighboring Emirates. However, the incidence rate remains relatively high. This study undertook a thorough review and assessment of the existing Fire Management System (FMS) for Sharjah's residential sector, leading to the recommendation of a more robust framework. Through a series of literature reviews, interviews with stakeholders, and surveys, using analytical techniques such as the Delphi method and Failure Mode Effect and Criticality Analysis, vital factors that influence the FMS's effectiveness were pinpointed. The study exposed that the current FMS was largely reactive. In response, a proactive framework incorporating nine essential components was designed to strengthen the Sharjah FMS. These components span across various domains such as fire assurance, assessment of fire risks, management of accidents, evaluation of performance, training programs, research and development initiatives, digital services, monitoring of regulatory compliance, firefighting strategies, automated responses to fires, recovery processes, as well as the use of resilient materials and architectural design considerations. Furthermore, the research introduced two specific fire indexes, the HRBFI and the EFRI. Anchored in the principles of Systems Thinking and Industry 4.0, this proposed framework is aimed at streamlining Sharjah's approach to fire management in dwellings, with the objective of diminishing the frequency of fire incidents yearly. The ultimate aspiration of this framework is to enhance the Emirate's standing in terms of safety and to guarantee a living environment that is safeguarded against the risk of fires..

Livability is one of the paramount characteristics of the current era that architects striving to achieve. This situation becomes even more important on commercial streets, whose functional diversity and constant movement of people require special action. In Erbil, commercial streets suffer from neglect in terms of adopting places for livability. Although it has huge potential to form an attractive and entertaining environment other than shopping. Some countries have developed standards for the design of commercial streets. The basic requirements are the same, but details usually vary according to the diverse needs of people. The research aims to identify a comprehensive framework (A Checklist) for placemaking to form the basis for measuring livability in one of the vital streets in Erbil city. This Framework is considered the basis for assessing and designing at the same time, supporting designers to achieve two main pillars: develop existing streets and suggest basic principles for future street design. This article adopts a practical framework, a questionnaire, a field survey, and observation as research methods. The research concluded that altogether placemaking dimensions are essential in activating livability, and a sort of balance between the main dimensions appeared. The more applied strategies, the higher the street livability.

The identification of bloodstain is one of the most important approaches in obtaining evidence in criminalistics. A threshold method based on spectral co-efficient and interclass variance is proposed in this paper, it is a non-contact, non-destructive method for quickly identifying bloodstains. The spectra of bloodstains and other suspected substances were all extracted from their hyper-spectral image. Then calculate the correlation coefficients of these spectral and interclass variances, analyze the differences between substances. The best blood recognition threshold was determined as 0.9. After preprocessing for eliminating systematic errors, experiments with the threshold 0.9 are carried out to identify bloodstains on the calico and red T-shirt. The method can remarkably identify the bloodstain from other non-blood substances both quickly and efficiently. The blood extraction rate can reach to 93.35% and 89.19%, respectively. It is an important step toward the implementation of bloodstain non-contact and non-destructive identification in forensic casework.

This study critically explores the legislative frameworks and national regulations of Serbia, Croatia, and Slovenia concerning the engagement, training, and responsibilities of search and rescue dogs and their handlers. The goal is to offer a thorough understanding of how these dogs and their handlers are engaged, trained, and managed in each country. By carefully analyzing the legal foundations, the study aims to clarify the rights, duties, and responsibilities of all parties involved, ensuring that search and rescue dog deployments are conducted within a structured and lawful framework. Such an understanding is crucial for improving disaster management and ensuring a coordinated response to various emergencies. The findings reveal that although each country has strong legal provisions for deploying search and rescue dogs, there are significant differences in the detail and scope of these regulations. Slovenia is notable for its detailed guidelines, regular evaluations, and licensing processes, setting a high standard. Croatia benefits from strong institutional support and active international collaboration, which greatly enhances its disaster response capabilities. On the other hand, Serbia, despite having a solid foundational framework, could improve by better coordinating with non-governmental organizations and aligning more closely with international standards. Key areas for improvement include harmonizing standards, enhancing training programs, increasing public awareness, better resource allocation, and adopting new technologies. By addressing these issues, Serbia, Croatia, and Slovenia can further strengthen their legal and institutional frameworks, ensuring their search and rescue teams are better prepared for disasters. This study emphasizes the need for continuous improvement through collaboration, standardization, and innovation to maximize the effectiveness of search and rescue operations involving dogs.

Estimating the economic risks of climate shocks and climate stressors on spatially heterogeneous cities over time remain highly challenging. The purpose of this paper is to present a practical methodology to assess the economic risks of climate change in developing cities to inform spatially sensitive municipal climate response strategies. Building on a capital-based framework (CBF), spatially disaggregated baseline and future scenario scores for economic wealth and its exposure to climate change are developed for six different classes of capital and across 77 major suburbs in Cape Town, South Africa. Capital-at-risk was calculated by combining relative exposure and capital scores across different scenarios and with population impacted plotted against the major suburbs and the city’s 8 main planning districts. The economic risk assessment presented here provides a generic approach to assist investment planning and the implementation of adaptation options through an enhanced understanding of relative levels of capital endowment vis-à-vis relative levels of exposure to climate-related hazards over time. An informed climate response strategy in spatially heterogeneous cities need to include spatially sensitive estimates on capital-at-risk and populations disproportionally impacted by climate exposure over time. The economic risk assessment approach presented here helps in advancing to such a goal.

IntroductionParamedicine is a domain of practice and health profession that specialises in the provision of health and social care across a range of settings including, but not limited to, emergency and primary care. Paramedics work in a variety of clinical settings such as paramedics services, hospitals, and clinics, in the community as well as non-clinical roles, such as education, leadership, policy work, public health and research. A previous competency framework for paramedics in Canada had become increasingly poorly aligned with contemporary practice. We sought to develop a competency framework that represents contemporary paramedic practice in Canada. MethodsThis project was guided by a conceptual framework informed by systems thinking and used a six-step model for developing competency frameworks. We engaged paramedics from across Canada in working groups to explore various contexts of paramedic practice. We gathered data using multiple methods and coded this data into activity statements. We grouped these statements and developed or expanded corresponding professional activities through five drafts of the framework from 2022 to 2024. A technical committee reviewed and edited the draft framework throughout the process, culminating in a public review period from January to March 2024, and a subsequent final version of the National Competency Framework for Paramedics (NCFP). ResultsThe National Competency Framework for Paramedics reflects contemporary paramedic practice in Canada. It describes the professional activities of paramedics organised under five domains - Person-Centred Care; Collaborative Care; Safe Care; Self-Care; and Professional Care. Paramedics perform these activities across multiple contexts of practice by enacting a variety of roles depending on the event. Discussion and conclusion By exploring the variety of practice contexts and the expectations of paramedics across Canada, the NCFP provides a holistic, contemporary description of paramedic practice in Canada, providing a foundation for professional practice in a variety of settings. The five domains provide a comprehensive representation of paramedic practice helping to ensure care is ultimately and sufficiently aligned with the needs of the community and settings in which paramedics can practice. The NCFP can be used to inform education programs to better prepare paramedics for complex, interprofessional health and social care practice now and as practice continues to evolve.

Background: Fiber-reinforced composites (FRCs) have been proposed as an alternative to traditional metal alloys for the realization of frameworks in full-arch implant supported prostheses. The aim of the present in vitro study was to evaluate the deflection under load of seven prostheses endowed with frameworks made of different materials, including different types of fiber reinforced composites (FRCs). Methods: A master cast with four implant analogues in correspondence of the two lateral incisors and the two first molars was used to create full-arch fixed prostheses with the same shape and different materials. Prostheses were made in different materials (framework+veneering material): gold alloy+resin (Au+R), titanium+resin (Ti+R), FRC with multi-directional carbon fibers+resin (ICFRC+R), FRC with unidirectional carbon fibers+composite (UCFRC+C), FRC with glass fibers+resin (GFRC+R), FRC with glass fibers+composite (GFRC+C), resin (R, full-acrylic prosthesis). Flexural tests were conducted using Zwick/Roell Z 0.5 machine and the deflection of the lower surface of the prosthesis was measured in order to obtain load/deflection graphs. Results: Greater rigidity and less deflection were recorded for UCFRC+C, GFRC+C, followed by Ti+R, and Au+R. The greatest deformations were observed for resin alone, ICFRC+R and GFRC+R. The results were slightly different in the incisal region probably due to the greater amount of veneering material in this area. Conclusions: When used to realize full-arch frameworks, Au and Ti allow a predictable mechanical behavior with gradual deformations with increasing load. UCFRC also demonstrated good outcomes and less deflection than ICFRCs when loaded. The GFRC full-arch framework may be a valid alternative, however showed greater deflections. Further studies are needed in order to evaluated how different prosthesis design and material thickness might affect the outcomes.

With the rapid advancement of Artificial Intelligence (AI), ensuring ethical safeguards is paramount, especially for powerful Large Language Models (LLMs). This paper delves into the challenges and implications of AI's transformative potential, particularly the risks associated with the generation of harmful content. A comprehensive review of existing ethical guidelines and risk assessment strategies is provided, highlighting notable efforts like the Asilomar AI Principles and the IEEE Ethically Aligned Design guidelines. The novel concept of "indelible ethical frameworks" is introduced, emphasizing the embedding of ethical constructs deep within AI systems to make them resistant to tampering. A critical analysis of this approach is presented, acknowledging its potential while addressing its challenges. The paper also explores the intricacies of ethically programming LLMs, emphasizing the significance of prompt engineering and the handling of unusual prompts. Through a series of witty exposés, the ethical journey of language models is narrated, likening their programming challenges to real-world ethical dilemmas. The piece concludes with proposed methodologies for researching AI safeguards, advocating for both reverse engineering complemented by ethical hacking and longitudinal monitoring with audit trails. These methodologies aim to reinforce the ethical integrity of AI systems, ensuring they are beneficial, transparent, and aligned with societal values..

With the recent advances in science and technology, more processing capability and data have become available, allowing a more straightforward implementation of data analysis techniques. Fortunately, the available online data storage capacity follows this trend, and vast amounts of data can be stored online freely or at accessible costs. As happens with every evolution (or revolution) in any science field, organizing and sharing this data is essential to contribute to new studies or validate the obtained results quickly. To facilitate this, we must guarantee interoperability between the existing datasets and the developed software, whether commercial or open source. This article explores this issue, analyzing the current initiatives to establish data standards and comparing some of the existing dataset online storage platforms. Through a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, it is possible to understand better the strategy that should be taken to improve the efficiency in this field, which directly depends on the data characteristics.

Competency frameworks outline the perceived knowledge, skills and other attributes required for professional practice. Competency frameworks have gained in popularity, in part for their ability to inform health professions education, assessment, professional mobility, and other activities. Previous research has shown inadequate reporting within reports describing their development and that may jeopardize their use and application. We aimed to develop a set of minimum criteria that provides guidance to authors (and consumers) in an effort to improve reporting of the development of competency frameworks. The checklist was developed by a 35-member expert panel and a five-member research team following published guidance from the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) Network. The final checklist contains 20 essential reporting items including guidance on reporting title and abstract, framework development, the development process, testing and funding/conflicts of interest. The intent of the COmpeteNcy FramEwoRk Development in Health Professions (CONFERD-HP) reporting guideline is to help readers (including researchers, educators, regulators, health professionals, and patients) develop a greater understanding of relevant terminology, core concepts, and key items to report for competency framework development in health professions.

This presentation is based on the article I wrote ((Lebese, 2018) where the A.C.A.I.D system was confirmed as directing human behavior in all situations. The A.C.A.I.D concept is an acronym. A stands for Attention, C for Consideration, A for Action, I for Internalisation and D for duplication. I developed the system over a period of twenty years as an impirical study, to answer a question: Why do I do what I do when I do what I do? Every behavior I exhibited followed specific steps from idea through to duplication. Once completed, I unobtrusively checked if other human beings behaved similarly. Without fail I observed that the A.C.A.I.D is a system that all humans knowingly and unknowingly follow. Through the system I could easily predict how the learners I was teaching could behave in particular situations. Also, I could assist learners who wanted change in their behaviors to know at what stage in the system that change was possible.The postgraduate scholar behaves in a predetermined manner in the research activity. Those behaviors follow an accepted protocol which invariably goes through all the stages of the A.C.A.I.D system. It starts with an idea through to the duplication step which I call “The idea on legs”. When it gets to this stage, it goes back to the beginning and sold as an idea. The system is cyclical.The A.C.A.I.D system as a framework helps the postgraduate researcher to follow an acceptable research praxis from idea interrogation through all the stages. This helps the researcher to follow acceptable predictable methodologis through research.

Lack of physical activity participation for adults with a disability remain a large public health concern. For those with existing, or newly acquired disability, increased sedentary behavior stemming from physical impairments often results in the development of secondary chronic health conditions (e.g., obesity or osteoarthritis) which leads to participation restrictions within major life roles. Without intervention these secondary conditions further increase physical impairment which, in turn, sets these populations up for continuous negative health trajectories over their remaining lifespan. Physical activity can attenuate the development of secondary health conditions and optimize health outcomes within these populations. However, those with disability often do not possess the necessary physical capacity to maintain physically active lifestyles. Therefore, exercise programs specifically designed to increase physical functioning have been identified as a necessary intermediate step to reduce physical limitations prior to adoption of physically active lifestyles. Adoption of exercise program participation remains a difficult task for both the general population and those with disability alike. Based on current rates of physical inactivity, it is clear that traditional health behavior change models do not adequately address the complexity of this issue. This paper highlights some of the limitations within the current health behavior change models as they relate to exercise behavior. Additionally, a novel conceptual framework is presented for the intent of its incorporation within research and health promotion interventions targeting exercise behavior within disability populations.

NS-3 is one of the most used Network Simulators because it includes different modules, applications and protocols to simulate a wide range of network. Since new proposals usually involve a large number of simulations, a procedure to automatize the simulation process and extract statistics is very useful. "Statistical Framework" (SF) is an important module of NS-3 because it fulfills this need. SF allows us to collect simulation data and obtain statistical reports for NS-3. In this report, we present a detailed description of statistical framework module and two useful examples of its configuration.

Styryl dyes are generally poor fluorescent molecules inherited from their flexible molecular structures. However, their emissive properties can be boosted by restricting their molecular motions. A tight confinement into inorganic molecular sieves is a good strategy to yield highly fluorescent hybrid systems. In this work, two Mg-aluminophosphate zeotypes (AEL framework, a one-dimensional channeled structure with elliptical pores of 6.5 Å x 4.0 Å; and CHA framework, composed by large cavities of 6.7 Å x 10.0 Å connected by 8-ring narrower windows) were selected for the encapsulation of 4-DASPI dye (trans-4-[4-(Dimethylamino)styryl]-1-methylpyridinium iodide). The synthesis and photophysical properties of 4-DASPI styryl dye occluded within these two types of confinements, AEL and CHA, are optimized and analyzed. The resultant hybrid systems display significantly improved photophysical features compared to 4-DASPI in solution. Molecular simulations reveal a tighter confinement of 4-DASPI in the elliptical channels of AEL, whilst a particular orientation is found within the CHA framework, where one 4-DASPI molecule spans along two adjacent cavities. In the latter scenario, each aromatic ring is positioned on these adjacent cavities and the polymethine chain resides within the 8-ring window, being this confinement slightly less tight than for AEL.

The hyperspectral vegetation index was defined based on the distinctive features of the spectral curve. In alignment with the growth and development characteristics of cotton, the spectral reflectivity of the cotton canopy was computed at different growth stages. The aim of this study was to clarify the association between cotton yield and canopy spectral indices and to develop yield estimation models utilizing hyperspectral imaging. The ASD Field Spec Pro VNIR 2500 spectrometer radiometer was employed to collect spectral reflectance data from cotton canopies at various growth stages. Using spectral analysis techniques, quantitative models were developed based on hyper-spectral vegetation indices, including Normalized Difference Vegetation Index (NDVI) (NDVI) and Radar Vegetation Index (RVI) to extract characteristic information from the cotton canopies. Following thorough testing and precise monitoring of the estimation models, the most optimal models representing cotton canopy structure parameters were identified. Results demonstrate that the power function model, relying on NDVI, provides the most accurate forecasting for Leaf Area Index (LAI)). Additionally, the exponential function model based on RVI proves to be the most effective for predicting cotton unit area above-ground fresh biomass, while another exponential function model based on RVI is identified as the best for predicting cotton unit area above-ground fresh biomass. Clearly, the application of hyper-spectral remote sensing technology enables the analysis, simulation, assessment, and providing scientific basis for precision cotton planting and cotton field management strategies.

In this paper, a relatively new hyper convolution spectra for characterization liquid, viscoelastic and solid samples is presented. Special attention is paid to the biophysical characterization of epithelial tissues and early detection of cancer of the skin, cervix, colon, and oral cavity. The method is based on the use of two types of white light, diffuse and polarized, from the same LED light sources. Based on the subtraction of the intensity values of these two lights and using the hyper convolution algorithm, the ratio of paired and unpaired electrons of tissue is obtained. This ratio correlates well with the state of the tissue: normal, irritating, precancerous, and cancerous. First, a proof-of-concept studies were performed, and later clinical studies on samples from one thousand two hundred fifteen people were performed. It showed that hyper Opto-Magnetic Imaging Spectroscopy (OMIS) can detect epithelial tissue cancers with an accuracy of 85-96%. Bearing in mind that the OMIS method also acquires the biophysical characteristics of the deeper skin layers (epidermis, basement membrane, dermis) by analysing convolution spectra of RGB channels of both diffuse and polarized white light, it is possible to apply this method in cosmetics (skin hydration, collagen, and elastin states, etc.). During OMIS applications, we noticed places where improvements could be made to increase method’s accuracy using advanced machine learning methodologies.

Hyper duplex stainless steels, HDSS, are a new alloy group of duplex stainless steels with the highest corrosion resistance and mechanical properties among the existing modern stainless steel. Due to the addition of extra high content of alloying elements, e.g. Cr, Ni, Mo, etc., the crystallization behavior of the δ-ferrite from the liquid is of vital importance for controlling the steel properties. In this work, the effect of cooling rate (i.e. 4°C/min and 150 °C/min) on the growth process of δ-ferrite in S33207 during the solidification process was investigated using high-temperature confocal scanning laser microscopy (HT-CLSM) in combination with electron microscopes and thermodynamic calculation. The results showed that the solidification mode of S33207 steel was a ferrite-austenite type (FA mode). L→δ transformation occurred at a certain degree of supercooling, and merging occurred during the growth of δ phase dendrites. Similar microstructures were observed after solidification under these two different cooling rates. The variation of area fraction of δ ferrite at the free surface and temperature as well as time during solidification of S33207 steels were calculated at different cooling rates of 4 °C/min and 150 °C/min, respectively. The current HT-CLSM study provides in-situ experimental evidence to confirm that a low cooling rate favored the growth of primary δ-ferrite, whereas a high cooling rate favored the nucleation of primary δ-ferrite during the solidification process. The post-microstructure as well as composition evolution is also briefly investigated. This work shed light on the real time insights for the crystallization behavior of hyper duplex stainless steels during solidification, which aims to contribute to the process control of manufacturing of this steel grade.

Low pressure fluid transport (1) applications often require low and precise volumetric flow rates (2) including low leakage to reduce additional costly and complex sensors. A peristaltic pump de-sign (3) was realized, with the fluid’s flexible transport channel formed by a solid cavity and the wobbling plate comprising a rigid and a soft layer (4). In operation, the wobbling plate is driven externally by an electric motor, hence, the soft layer is contracted and unloaded (5) during pump-cycles transporting fluid from low to high pressure sides. A thorough characterization of the pump system is required to design and dimension the components of the peristaltic pump. To capture all these parameters and their dependencies on various operation-states, often complex and long-lasting dynamic 3D FE-simulations are required. We present, here, a holistic design methodology (6) including analytical as well as numerical calculations, and experimental valida-tions for a peristaltic pump with certain specifications of flow-rate range, maximum pressures, and temperatures. An experimental material selection process is established and material data of candidate materials (7) (liquid silicone rubber, acrylonitrile rubber, thermoplastic-elastomer) are directly applied to predict the required drive torque. For the prediction, a semi-physical, analyti-cal model was derived and validated by characterizing the pump prototype.

This paper presents Double Database Decomposition Framework, which integrates vertical and horizontal table decompositions to minimize data leakage due to intrusions. Vertical decompositions segregate sensitive attributes, while horizontal ones partition data based on user access patterns. In this way, the framework ensures that users access only the data necessary for their operations, adhering to the principle of least privilege. This double decomposition approach improves the robustness of the original database against impersonation attacks and limits the blast radius of potential intrusions. Furthermore, the framework significantly mitigates the risks associated with data breaches by confining unauthorized access to specific data subsets and restricting the exposure of sensitive information. Performance analysis highlights the trade-offs between robustness and overhead offered by distinct allocation strategies of the output of the decompositions to, among other, physical machines, virtual machines, and containers, to balance security and resource efficiency. We present a case study in a healthcare environment that confirms both the effectiveness of the framework and its applicability in complex systems where data security is paramount. By integrating advanced security measures and optimising data access, the framework results in a scalable and adaptable solution for enhancing database security and performance in various domains.

With the global supply of LNG experiencing unprecedented growth, the industry is on the brink of a programmed glut, further exacerbated by the challenges posed by weaker energy pricing. In light of this, it is expected that marginal programs will be deferred temporarily, yet continuously assessed to enhance their economic potential in the future. Moreover, recent setbacks in LNG programs, such as cost overruns and delays, highlight the critical need for a unique conceptual design master plan. This plan will serve as a powerful tool to evaluate and optimize new programs, ensuring improved economic viability and boosting rates of return. By leveraging advanced analytical frameworks, innovative technologies, and comprehensive risk assessments, this study aims to develop a transformative approach that drives sustainable growth and profitability in the dynamic landscape of LNG program development." "This paper centers around the development of a comprehensive conceptual design master plan aimed at optimizing the total program economics across various areas, including sub-sea, offshore, and onshore advancements. The master plan serves as a crucial framework for identifying opportunities to reduce costs and enhance program definition, drawing insights from selective benchmarks derived from recent successful LNG programs. The ultimate objective is to boost the program rate of return while improving overall program definition, thereby mitigating the risks of cost overruns and schedule delays that have plagued the industry. By implementing this master plan, marginal programs stand a greater chance of becoming economically viable, fostering sustainable growth and enabling stakeholders to make informed decisions in the highly dynamic LNG landscape.

European cities are motivated to act towards the achievement of climate-neutral mobility solutions. Often, though, they are facing many challenges when bringing (innovative) sustainable mobility solutions forward. Capacity building that fills skills gap and/or enables acquisition of new ones related to the planning and implementation of such solutions can empower local/ regional authorities to identify them, adopt them and eventually deliver them properly. The aim of this paper is to present the Key Performance Indicator (KPI) framework that has been used for the assessment of the effectiveness of the Learning and Exchange Programme applied in an EU-funded project. It presents the methodological steps for the adoption of the KPIs, as well as the tools used for the selection of the KPI data and the KPI monitoring at project level. It also presents the results from the application of the framework for assessing the knowledge performance towards the deployment of sustainable mobility solutions. It finally reflects on recommendations for applying the KPI framework to other cases and thematic contents.

Decision makers have to work on resolving multiple forms of problems within their organizations. The problems can be structured, semi-structured, or unstructured. Handling these issues requires intensive time dedication and resource allocation. Looking at the perspective of strategic decision making in Saudi Arabia, current operations are lacking in various sectors. This indulges an immediate need to introduce proper systems and highlight the loopholes that would allow the leaders make informed decisions aided by a support system. For this reason, the researchers set out to work on two research questions. The first question focused on how decision makers ensured accuracy of the decisions they are making. It would allow to identify why such system is important that should also guarantee consistency and accuracy. The second question asked about the proper process that would govern and control the outcome of decision. Considering the above questions, this research aimed at identifying the ideal framework, and whether the proposal developed would fit into the Saudi organizations. A case study in health sector has been reviewed containing various models across the world. Following this, interviews with five decision makers in their organization were conducted to perform the qualitative research. The researcher identified that most organizations lacked systems to ensure their decisions were accurate. The research also concluded that the tools and elements presented in the proposed informed decision framework would fit into most Saudi organizations to eliminate the identified problem, and especially due to the inclusion of the non-digital sources of information as part to the decision process. Furthermore, the analysis was conducted and discussion was determined stating validations of the study.

Competency frameworks are developed for a variety of purposes, including describing professional practice and informing education and assessment frameworks. Despite the volume of competency frameworks developed in the healthcare professions, guidance remains unclear and is inconsistently adhered to (perhaps in part due to a lack of organising frameworks), there is variability in methodological choices, inconsistently reported outputs, and a lack of evaluation of frameworks. As such, we proposed the need for improved guidance. In this paper we outline a six-step model for developing competency framework that is designed to address some of these shortcomings. The six-steps comprise [1] identifying purpose, intended uses, scope, and stakeholders; [2] theoretically informed ways of identifying the contexts of complex, ‘real-world’ professional practice, which includes [3] aligned methods and means by which practice can be explored; [4] the identification and specification of competencies required for professional practice, [5] how to report the process and outputs of identifying such competencies, and [6] built-in strategies to continuously evaluate, update and maintain competency framework development processes and outputs. The model synthesizes and organizes existing guidance and literature, and furthers this existing guidance by highlighting the need for a theoretically-informed approach to describing and exploring practice that is appropriate, as well as offering guidance for developers on reporting the development process and outputs, and planning for the ongoing maintenance of frameworks.

Background: In an age where information is generally accessible, most of the interest these days has focused on how accessible and convenient technology can be. So small and personal, mobile devices can transform our perception of learning by combining both mobility and convenience. Mobile learning is part of the digital learning landscape alongside e-learning and serious games. However, knowledge about effective design of mobile learning experiences remains of interest with a focus on appropriate design models and the embodiments that can be implemented to achieve the intended educational outcomes. Exploring the instructor's perspective on mobile learning is essential. Therefore, the aim of this study was to investigate the Moroccan instructors' perception and practice of mobile learning to inform the development of an ecologically valid mobile learning integration model. Methods: Higher education Instructors (n=41) were recruited to the study. The Moroccan instructors' perception and their experiences regarding their adoption of mobile learning were collected using an online survey. The analysis focused on their mobile use, perceived IT competency, and opinions on mobile learning. Results: We described most of the instructors' considerations regarding integrating mobile technologies into their teaching activities. We found that most of the mobile learning activities defined by the respondents corresponded to relatively advanced use of mobile devices. More promising, instructors have found innovative ways to use the educational potential of mobile devices. However, the prospect of mobile devices was still to challenge. No or poor Wi-Fi connection, number of devices or limited access, sometimes fees or applications incompatibility were identified as reasons and obstacles to mobile learning usage. Conclusion: Mobile learning is mostly perceived positively among Moroccan instructors allowing many applications and usage to enhance teaching and learning. In this study, a better understanding of aspects and factors influencing the integration of mobile learning in the Moroccan educational context is exposed, helping further the development of an ecologically valid mobile learning integration model. Future work on mobile learning should consider the highly paced evolution of mobile technologies, emphasizing the flexibility of integration frameworks to support instructors and learners.

An array of Ag nanowires has been prepared from a facile, templated approach on Cu(BTC) (1,3,5-benzenetricarboxylic acid) metal organic framework (MOF) micropillars. The Ag-deposited scaffolding material may be used to prepare electronic or optoelectronic devices for various applications.

Structure/material requires simultaneous consideration of both its design and manufacturing processes to dramatically enhance its manufacturability, assembly and maintainability. In this work, we present a novel design framework for structure/material with requested mechanical performances in virtue of the compelling properties of topological design and origami techniques. The framework comprises four procedures, including topological design, unfold, reduction manufacturing, and fold. Topological design method, i.e. Solid Isotropic Material Penalization (SIMP) method, serves to optimize the structure to achieve preferred mechanical characteristics and origami technique is exploited to make the structure rapidly and easily fabricated. Topological design and unfold procedures can be conveniently completed in a computer; then, reduction manufacturing, i.e. cutting, is performed to remove materials from the unfolded flat plate; the final structure is finally obtained by folding the plate of the previous procedure. A series of cantilevers, consisting of origami with parallel creases and Miura-ori (usually regarded as a metamaterial), made of paperboard are designed with least weight and required stiffness by using the proposed framework. The findings here furnish an alternative design framework for engineering structures which could be better than 3D printing technique, especially for large structures made of thin metal materials.

Deficient wound healing is frequently observed in patients diagnosed with diabetes, a clinical complication that compromises mobility and leads to limb amputation, decreasing patient autonomy and family lifestyle. Fibroblasts are crucial for secreting the extracellular matrix to pave the wound site for endothelial and keratinocyte regeneration. The biosynthetic pathways for collagen production are one of the main components of the extracellular matrix, as the crosslinking of extracellular collagen fibers is intimately related to fibroblast redox homeostasis. In this study, human dermic fibroblasts were cultured with 1 mM sodium selenite (inorganic) and 1 mM of two selenium amino acids (organic), Se-cysteine and Se-methionine. This concentration was compatible with long-term exposure necessary for extracellular matrix production and allowed us to examine the impact of these three compounds on the state and response of the thiol-based HyPer biosensor expressed in the cytoplasm. In addition, the abundance of extracellular matrix and ultrastructural properties were evaluated by Picrus Sirius red staining and scanning electronic microscopy, respectively. We also evaluated whether these selenium compounds effectively ameliorated the perturbations induced by culturing fibroblasts at high glucose levels (25 mM) regarding cytoplasmic redox, extracellular matrix, and glycation on collagen fibers. Our results indicate that cytoplasm protein oxidation was sensitive to selenium compound supplementation. Selenium amino acids increased the sensitivity to protein oxidation, and only Se-cysteine increased the disulfide bond reduction in fibroblasts maintained in high glucose. Despite the lack of effect of selenium compounds on the glu-cose-induced increase in matrix fiber thickness, we found that endothelial migration improved in the matrix generated by high glucose-cultured fibroblasts.

The soil and groundwater of Bhagobangola I block of Murshidabad district, West Bengal, India is severely arsenic contaminated. A bacterium was isolated from the garden soil of Mahishasthali village, which could tolerate 36.49 mM arsenic (III), 280.44 mM arsenic (V) and 63 mM chromium (III), which makes it arsenic (III & V) and chromium (III) hyper-tolerant bacterium. The growth pattern of this bacterium does not show much alteration in presence of 10 mM arsenic (III) and chromium (III), emphasizing its resistance to these heavy metals. Scanning electron microscopy depicted the size of this bacterium to be ~1.45 µm. 16S rDNA sequencing followed by subsequent phylogenetic analysis established the identity of this bacterium as Microbacterium paraoxydans. The bacterium is capable of bioremediation of arsenic and showed 30.8% & 22.6%, and 35.2% and 30.5% of bioremediation over a period of 24 and 48 hours in 1 mM and 4 mM arsenite, respectively. Microbacterium paraoxydans also exhibit plant growth promoting properties like nitrogen fixation, phosphate solubilization, indole-3-acetic acid production and production of siderophores. Therefore, the heavy metal resistance, bioremediation potential and plant growth promoting potential of the bacterium could be utilized not only for reduction of arsenic toxicity in soil and groundwater but also for plant growth promotion by using it as a biofertilizer.

Genetic Programming (GP) is a powerful Machine Learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in Remote Sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs Feature Construction by evolving hyper-features from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyper-feature from satellite bands to improve the classification of land cover types. We add the evolved hyper-features to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (Decision Trees, Random Forests and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyper-features to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI and NBR. We also compare the performance of the M3GP hyper-features in the binary classification problems with those created by other Feature Construction methods like FFX and EFS.

Hyper spectral imaging (HSI) is a technique that is used to obtain the spectrum for each pixel in the image. It helps in finding objects and identifying materials etc. Such an identification is very difficult using other imaging techniques. It allows the researchers to investigate the documents without any physical contact. Nowadays detection of unequal Ink mismatch based on HSI has shown vast improvement in distinguishing the inks. Detection of unequal Ink mismatch is an unbalanced clustering problem. This paper used K-means Clustering for ink mismatch detection. K-means Clustering find same subgroups in the data based on Euclidean distance. This paper demonstrates performance in unequal Ink mismatch based on HSI.

This paper explores Ghana's two-decade journey in developing a digital land information system (LIS), exemplified by Ghana Enterprise Land Information System (GELIS) and Enterprise Land Information System (ELIS). Despite advancements by Ghana’s Lands Commission, manual processes persist and coexist with digital ones. Our study uses a qualitative approach to assess the effectiveness of the LIS implemented in Accra. The study found that Accra’s LIS is highly aligned with clear institutional mandates, roles, and responsibilities as well as the availability of laws and policies to support analogue to digital conversion. Furthermore, a robust approach for protecting data, operating system and software underpins the system. Subsequently, the integration of a digital LIS has enhanced service delivery and accountability. Nonetheless, Accra’s LIS is still at the beginning of a comprehensive learning and development curve. To sustain the LIS requires furtherance in the implementation plan, funding, law implementation, ICT strategy, divisional integration, work processes, data quality, and communication strategy. These findings will inform the next steps of improvement for Accra's LIS and guide its nationwide scaling, contributing to discussions on technology acceptance dynamics in predominantly analogue environments.

This paper investigates nonverbal communication in human interactions, with a specific focus on facial expressions. Employing a Long Short-Term Memory (LSTM) architecture and a custom-ized facial expression framework, our approach aims to improve virtual agent interactions by incorporating subtle nonverbal cues. The paper contributes to the emerging field of facial expres-sion generation, addressing gaps in current research and presenting a novel framework within Unreal Engine 5. The model's architecture, trained on the CANDOR corpus, captures temporal dynamics, and refines hyperparameters for optimal performance. During testing, the trained model showed a cosine similarity of -0.95. This enables the algorithm to accurately respond to non-verbal cues and interact with humans in a way that is comparable to human-human interac-tion. Unlike other approaches in the field of facial expression generation, the presented method is more comprehensive and enables the integration of a multi-modal approach for generating facial expressions. Future work involves integrating blendshape generation, real-world testing, and the inclusion of additional modalities to create a comprehensive framework for seamless hu-man-agent interactions beyond facial expressions.

The need for systemic healthcare policies to systematically eliminate NTDs globally and in India has been stressed for more than two decades. Yet, the present policies and the research on them do not meet the need. We present an ontological framework, a research roadmap, and a policy brief to address the gap. The ontology clearly, concisely, and comprehensively represents the combinations of diseases, the objectives regarding the diseases, the entities to address them, the outcomes sought, and the potential policy instruments to invoke. The paper explicates the state-of-the-policies and state-of-the-research on policies to eliminate NTDs in India. It highlights the significant gaps in both. Last, it presents a set of systemic policies congruent with the ontology to systematically address the gaps. The recommendations are aligned with the present research, policies, practices, and recommendations in India and of WHO, UN agencies, and other similar bodies. The approach can be generalized to provide roadmaps for other countries facing a similar challenge and for other diseases of similar complexity. The roadmaps, with continuous feedback and learning, can help navigate the challenge efficiently and effectively.

Community co-creation is increasingly crucial to the building of sustainable societies. Communities must proactively and independently create value chains in the face of many wicked problems. However, practical frameworks and tools for facilitating community co-creation require further development. As such, for this paper, we propose a theoretical framework for analyzing community co-creation value chain mechanisms based on a narrative review of literature on Collective Impact, social impact assessment, and Community Capital from the perspective of collective impact, a framework with a fair amount of previous research. We argue for the importance of applying knowledge of social impact assessment, particularly for the collective impact conditions of common agenda and shared measurement system. We discuss its relationship with the Theory of Change and the use of systems thinking to mitigate the subjectivity and arbitrariness of design thinking. Furthermore, we emphasize the value of employing the community capital concept as a framework for analyzing interrelationships among elements. By integrating multiple concepts, we enhance the collective impact framework and outline potential avenues for future research on analyzing and effectively implementing co-creation value chain mechanism in a community-based approach.

The growth in the Internet of Things (IoT) devices in the healthcare sector enables the new era of the Internet of Medical Things (IoMT). However, IoT devices are susceptible to diverse cybersecurity attacks and threats that lead to negative consequences. Cyberattacks can harm not just the IoMT devices being used but also human life. Currently, several security solutions are proposed to enhance the security of the IoMT, which uses machine learning (ML) and blockchain. ML can be used to develop detection and classification methods to identify cyberattacks targeting IoMT devices in the healthcare sector. In addition, blockchain technology enables a decentralized approach to the healthcare system and eliminates some disadvantages of a centralized system, such as a single point of failure. This paper proposes a resilient security framework integrating a Tri-layered Neural Network (TNN) and blockchain technology in the healthcare domain. The TNN detects anomalies in data measured by medical sensors to find fraudulent data. Therefore, cyberattacks are detected and discarded from the IoMT system before data is processed at the fog layer. In addition, a blockchain network is used in the fog layer to ensure that the data is not altered, boosting the integrity and privacy of the medical data. The experimental results show that the TNN and blockchain models produce the expected result. Furthermore, the accuracy of the TNN model reached 99.99% on the F1-score accuracy metric.

Metal organic frameworks (MOFs) composed of metal ions and multifunctional organic ligands are one of the most attractive porous materials. Their potential applications in gas storage, separation, catalysis, biomedicine and many other fields have attracted much attention. In this study, Nano-Mn-MOF-74 with nano size were successfully synthesized by adding acetic acid and characterized it by SEM, TEM and XRD. Furthermore, a new method to trace the degradation process of nanoMOFs through detection of ligand concentration under physiological conditions was developed.

This paper provides an overview of the current state-of-the-art digital twin and digital thread technology in industrial operations. Both are transformational technologies that have the advantage of improving the efficiency of current design and manufacturing. Digital twin is an important element of the Industry 4.0 digitalization process; however, the huge amount of data that are generated and collected by digital twin offer challenges in handling, processing and storage. The paper aims to report on the development of a new framework that combines the digital twin and digital thread for better data management in order to drive innovation, improve the production process and performance, and to ensure continuity and traceability of information. The digital twin/thread framework incorporated behavior simulation and physical control components, in which these two components rely on the connectivity between the twin and thread for information flow and exchange to drive innovation. The twin/thread framework encompasses specifications that include organizational architecture layout, security, user access, cloud computing set-up, and hardware and software requirements. It is envisaged that the framework will be applicable to enhancing optimization of operational processes and traceability of information in the physical world, especially in Industry Shipyard 4.0.

In recent years, advances in science, technology, and the way in which we view our world have led to anincreasingly broad use of the term “intelligence”. As we learn more about biological systems, we find more and more examples of complex and precise adaptive behavior in animals and plants. Similarly, as we build more complex computational systems, we recognize the emergence of highly sophisticated structures capable of solving increasingly complex problems. These behaviors show characteristics in common with the sort of complex behaviors and learning capabilities we find in humans, and therefore it is common to see them referred to as “intelligent”. These analogies are problematic as the term "intelligence" is inextricably associated with human-like capabilities. While these issues have been discussed by leading researchers of AI and renowned psychologists and biologists highlighting the commonalities and differences between AI and biological intelligence, there have been few rigorous attempts to create an interdisciplinary approach to the modern problem of intelligence. This article proposes a comparative framework to discuss what we call “purposeful behavior”, a characteristic shared by systems capable of gathering and processing information from their surroundings and modifying their actions in order to fulfill a series of implicit or explicit goals. Our aim is twofold: on the one hand, the term purposeful behavior allows us to describe the behavior of these systems without using the term "intelligence", avoiding the comparison with human capabilities. On the other hand, we hope that our framework encourages interdisciplinary discussion to help advance our understanding of the relationships among different systems and their capabilities.

Medical health systems have been concentrating on artificial intelligence techniques for speedy diagnosis. However, the recording of health data in a standard form still requires attention so that machine learning can be more accurate and reliable by considering multiple parameters. The aim of this study is to develop a general framework for recording diagnostic data in an international standard format to facilitate auto-prediction of eye diseases. Efforts were made to ensure error-free data entry by developing a user-friendly interface. Furthermore, different machine learning algorithms were used to analyze patient data based on multiple parameters, including age, illness history, and clinical observations. This data was formatted according to structured hierarchies designed by medical experts, whereas diagnosis was made as per the ICD-10 coding developed by the American Academy of Ophthalmology. Furthermore, the system is designed to evolve through self-learning by adding new classifications for both diagnosis and symptoms. The classification results from tree-based methods demonstrated that the proposed framework performs satisfactorily given a sufficient amount of data. The random forest and decision tree algorithms predicted more accurately as compared to neural networks and the na{\"i}ve Bayes algorithm owing to structured data arrangement.

This paper present a platform for airborne sensor applications using low-cost, open-source components carried by an easy-to-fly unmanned aircraft vehicle (UAV). The system, available in open-source [1], is designed for researchers, students and makers for a broad range of their exploration and data-collection needs. The main contribution is the extensible architecture for modularized airborne sensor deployment and real-time data visualisation. Our open-source Android application provides data collection, flight path definition and map tools. Total cost of the system is below 800 dollars. The flexibility of the system are illustrated by mapping the location of Bluetooth beacons (iBeacons) on a ground field and by measuring water temperatures in a lake.

This paper introduces new improvements to the modified version of the BIRECT (BI secting RECTangles) algorithm referred to as BIRECTv. We explore various approaches, by first including a grouping strategy for hyper-rectangles having almost the same sizes by categorizing them into different classes. Therefore constraining them not to exceed a certain pre-defined threshold (a small positive value to define the tolerance level). This approach allows for more efficient computation and can be particularly useful when dealing with a large number of hyper-rectangles with varying sizes. To avoid over-sampling, and preventing redundant or excessive sampling, at some shared vertices in descendant subregions, a particular vertex database is used to limit the number of samples taken within each subregion to two. The experimental investigation shows that these improvements have a positive impact on the performance of the BIRECTv(imp.) algorithm and the proposal is a promising global optimization algorithm compared to the original BIRECTv algorithm and its variants. Additionally, the BIRECTv(imp.) algorithm showed particular efficacy in solving high-dimensional problems.

U-Net, based on a deep convolutional neural network (CNN), has been clinically used to au-to-segment normal organs and potentially target volumes. However, CNNs with local geometric dependencies may limit the accuracy of segmentation. Additionally, the performance of CNNs can vary depending on the selection of network hyper-parameters, which was mitigated by the proposition of nnU-Net. We chose a vision transformer architecture called VT U-Net, which features a self-attention excluding the convolution layer, to overcome the limitations of CNNs by utilizing global geometric information of images. The VT U-Net v.2 became more powerful thanks to the adaptive hyper-parameter optimizer embedded in nnU-Net. However, despite leveraging the benefits of nnU-Net, VT U-Net v.2 still had additional network hyper-parameters that needed to be optimally chosen. Accordingly, among various hyper-parameters, this study attempted to find the optimal combination of the patch size and the embedded dimension regarding the transformer. From the 4-fold cross-validation, the modified VT U-Net v.2 showed the highest average performance for planning target volume (PTV) segmentation among the investigated networks. Though nnU-Net was based on convolution layers, the adaptive hyper-parameter optimizers turned out to enhance the performance. It was also confirmed that network hyper-parameters affected the segmentation accuracy of vision transformers.

The ever-evolving ways attacker continues to im prove their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. In this research, we proposed an adaptable framework that combines Deep learning and Randon Forest to read images, synthesize speech from deep-fake videos, and natural language processing at various predictions layered to significantly increase the performance of machine learning models for phishing attack detection.

In recent years, concerns regarding the environmental impact of microplastics (MPs) have led to increased international attention on these pollutants. Although initial focus was largely directed toward marine environments, land-based pollution sources, including MP release, has been recognized to directly affect marine ecosystems. Therefore, soil-, atmosphere-, groundwater-, and river-based research is ongoing. However, when considering sources of MP, it is necessary to examine the circular system of plastic in terms of raw materials, production, consumption, discharge, and disposal (recycling). Accordingly, the present study proposes a strategy to effectively manage MPs using this circular system. First, the factors influencing MPs in the circular system were identified, and MPs at the system’s final stage, i.e., at the waste treatment facility, were subsequently investigated. Using the concept of MP waste (MPW), strategies were then developed for effective MP management within the circular system. Applying the proposed theoretical strategy to the Korean waste management system revealed that the new policy framework improves the current MP management system. Overall, this study provides fundamental data for establishing new or improved MP management schemes from a waste sector perspective.

As the number of Small and Medium Enterprises (SMEs) rises in the world, the amount of sensitive data used also increases, making them targets for cyberattacks. SMEs face a host of issues such as lack of resources, and poor cybersecurity talent, resulting in multiple vulnerabilities that increase overall risk. Cybersecurity risk assessment frameworks have been developed by multiple organizations such as the National Institute of Science and Technology (NIST) and the International Organization for Standardization (ISO), but they are complicated to understand, and challenging to implement. This research aimed to create an effective cybersecurity risk assessment framework specifically for SMEs while considering their limitations. This was achieved by first identifying common threats and vulnerabilities and categorizing them according to their importance, and risk. Secondly, popular frameworks like the NIST CSF and ISO 27001/2 were analyzed for their proficiencies and deficiencies while identifying relevant areas for SMEs. Finally, novel techniques catered to SMEs were explored and incorporated to create an effective framework for SMEs. This framework was also developed in the form of a tool, providing an interactive and dynamic environment. The tool was effective and the framework is a promising start but requires more quantitative analysis.

This paper explores the development of a sophisticated system for automatic test generation tailored specifically for Java web applications utilizing REST interfaces. Writing tests ensures the quality of the software solution event after the introduction of subsequent changes, because automated regression testing can detect potential bugs in the code event before the application is resealed into production. By utilizing and comparing various automatic test generators, the system described provides users with a visual representation of code structure and coverage, enhancing insights through call graph visualization. The paper describes the concepts of theoretical automatic test generators, gives an overview of generators, and details the design and implementation of the implemented solution, which integrates the system test generator and the unit test generator based on open source to achieve the maximum code coverage level.

Despite much published literature on the impacts of agriculture on water quality, knowledge gaps persist regarding which farming systems are of most concern for these relationships, which could help water resource planners better target water management efforts. This study addresses these subjects, seeking to understand how this relationship varies across different farming systems. We used data on water quality status in watersheds of an agricultural region in southern Portugal and crossed it with a map of farming systems for the same region provided by a previous study. By overlaying both data layers, we characterized the areal shares of the farming systems in the watersheds and inspected how these shares relate with water quality status through logistic regression. Results support that the impact of agriculture on water quality is mostly related with specific farming systems. We believe this type of information can be of high interest for agricultural planners and policymakers interested in meeting water quality standards, and we conclude by suggesting innovative policy options based on payments to farmers operating selected farming systems, as a cost-effective way to reconcile agricultural and environmental policy objectives.

Oral Squamous Cell Carcinoma is one among the most common cancer and early detection is the main key to avoid deaths. Automated diagnostic tools that process the histopathological images of a patient to detect abnormal oral lesions will be very much useful for the clinicians. A deep learning framework have been designed with an intermediate layer between feature extraction layers and classification layers for classifying the histopathological images into two categories, namely normal and oral squamous cell carcinoma. The intermediate layer is constructed using the proposed Swarm Intelligence technique called Modified Gorilla Troops Optimizer. Various optimization algorithms are implemented in literature for optimal parameter identification, weights updating and feature selection in deep learning models, but this work focuses on usage of optimization algorithm as intermediate layer that transforms the extracted features into the features that are more suitable for classification. Three datasets totally comprising 2784 normal and 3632 oral squamous cell carcinoma subjects are considered in this work. Three popular CNN architectures namely InceptionV2, MobileNetV3, and EfficientNetB3 are investigated as feature extraction layers. Two fully connected Neural Network layers along with batch normalization and dropout are used as classification layers. Among the investigated feature extraction models, MobileNetV3 performs well in all the three datasets with the highest accuracy of 0.89. Usage of the proposed Modified Gorilla Troops Optimizer as an intermediate layer boosts this accuracy to 0.95.

Machinery parts gradually wear out over time due to regular usage. To improve machinery health and prevent critical issues, a reliable prognosis framework can be implemented by monitoring the behaviour of machinery parts and issuing warnings before they reach a critical state. To achieve this, vibration data from roller bearings experiencing various fault conditions have been collected. Different techniques from the literature were combined to analyze the distinct configurations in the vibration data sets and identify the main defects in roller bearings. The significant features extracted from this analysis were then used to create optimized stochastic model equations, separately regressing inner and outer race fault features to healthy bearing features under random conditions. These models can help engineers design more dependable systems, optimize their performance, and minimize the risk of failures and downtime.

With the rise of serious problems of urbanization and climate change, the call for intelligent and sustainable urban development is increasing nowadays. Therefore, smart cities have become a hot topic in academia, industry, and politics, resulting in the emergency of numerous smart city programs and projects. This study selected 26 evaluation frameworks proposed in recent years or large-scale research projects that have lasted for many years and analyzed and compared them from the 7 aspects of generalizability, comprehensiveness, availability, flexibility, scientific, transparency, and interpretability. The result indicates that the current smart city evaluation framework still needs to be upgraded and updated. These standards can not only guide the development direction and hotspot of the future smart city evaluation model but also serve as a guide for model demanders to test results and an effective tool to verify the framework designed by themselves.

Abstract The last mile has to deliver the goods at the right time and at the right cost for business logistics services to enhance the customer service experience. The objective of this paper is to examine the challenges of retail omni-channel distribution influencing the effective last mile fulfilment operations processes in the apparel group (organisation phase). It further establishes the extent of how the last mile for demand-driven store replenishment influences the level of retailers’ order fulfilment (in full and on time) (environment phase). This paper, persistently assesses the contribution of integrated information systems on brick and mortar and virtual omni-channel distribution networks to improve timeousness, order accuracy and fill rate (technology phase). The mixed method has triangulated the methods from both quantitative methodology using frequencies and binomial testing and subjective thematic analysis of qualitative data from interviewees. The sample of 175 questionnaires were received with a response rate is 85 per cent (148 responses). The main findings of the paper revealed that the current order fulfilment process aligned with the last mile is effective on the basis of the fill rate, lead time and order condition of orders delivered via store economy and store express.

As organizations are vulnerable to cyber attacks, their protection becomes a significant issue. Capability Maturity Models can enable organizations to benchmark current maturity levels against best practices. Although many maturity models have been already proposed in the literature, a need for models that integrate several regulations exists. This article presents a light web-based model that can be used as a cybersecurity assessment tool for Higher Education Institutes (HEIs) of the UK. The novel Holistic Cybersecurity Maturity Assessment Framework incorporates all security and privacy regulations and best practices that HEIs must be compliant to and can be used as a self-assessment or a cybersecurity audit tool.

In 2010, Parties to the Convention on Biological Diversity (CBD) adopted the Strategic Plan for Biodiversity 2011–2020 to address the loss and degradation of nature. Subsequently, most biodiversity indicators continued to decline. Nevertheless, conservation actions can make a positive difference for biodiversity. The emerging Post-2020 Global Biodiversity Framework has potential to catalyze efforts to ‘bend the curve’ of biodiversity loss. Thus, the inclusion of a goal on species, articulated as Goal B in the Zero Draft of the Post-2020 Framework, is essential. However, as currently formulated, this goal is inadequate for preventing extinctions, and reversing population declines; both of which are required to achieve the CBD’s 2030 mission. We contend it is unacceptable that Goal B could be met while most threatened species deteriorated in status and many avoidable species extinctions occurred. We examine the limitations of the current wording and propose an articulation with robust scientific basis. A goal for species that strives to end extinctions and recover populations of all species that have experienced population declines, and especially those at risk of extinction, would help to align actors towards the transformative actions and interventions needed for humans to live in harmony with nature.

Cigarette butts, also known as tobacco product waste (TPW), are the single most collected item in environmental trash cleanups worldwide. This study used an online survey tool (Qualtrics) to assess knowledge, attitudes, and perceptions about this issue among individuals representing the Framework Convention Alliance (FCA). The FCA has about 683 members on its listserv, including non-governmental tobacco control advocacy groups that support implementation of the World Health Organization’s (WHO) Framework Convention on Tobacco Control (FCTC). Respondents (n = 65) represented countries from all six WHO regions. The majority (82%) had heard the term TPW, and all considered TPW as an environmental harm at some level. Additionally, 29% of respondents failed to identify that “cigarette filters make smoking easier.” Most (73%) correctly identified TPW components; however, fewer (60%) correctly identified the composition of cigarette butts. The majority (57%) were unfamiliar with Extended Producer Responsibility (EPR) and Product Stewardship (PS) as possible environmental intervention strategies. Respondents expressing opinions concurred that adding a litter fee to fund TPW programs will aid in reducing tobacco use and reduce the environmental impacts of TPW (100%); that prevention, reduction, and mitigation of TPW could be an important part of international tobacco control programs (98%); and that banning smoking in outdoor venues could reduce TPW (95%). Only 16% reported effective prevention or clean-up efforts in their countries. Weighted rankings revealed that respondents’ saw the national government, the tobacco industry, and state governments as most important in addressing TPW. The results of this research will inform continuing international discussions by the FCTC Conference of the Parties (COP) regarding environmental policies that may be addressed within FCTC obligations.

The introduction of innovative digital tools for supporting manufacturing processes has far-reaching effects on an organizational and an individual level due to the development of Industry 4.0. The FACTS4WORKERS project funded by H2020, i.e. Worker-Centric Workplaces in Smart Factories, aims to develop user-centered assistance systems in order to demonstrate their impact and applicability at the shop floor. To do so it is important to understand how to develop such tools and how to assess if advantages can be derived from the created ICT system. This study introduces the technology of a workplace solution that is linked to a specific industrial challenge. Subsequently, a 2-stepped approach to evaluate the presented system is discussed. Heuristics, which are an output of project “Heuristics for Industry 4.0”, are used to test if the developed solution covers critical aspects of socio-technical system design. Insights into the design, development and holistic evaluation of digital tools at the shop floor should be shown.

The purpose of the present research was to analyze the available data on river restoration projects. As the framework of our study, we conducted a structured international survey. We asked selected entities and experts from among those responsible for river restoration in European countries about the details and costs of European Union river restoration projects. We examined 119 river restoration projects that were implemented in Europe between 1989 and 2016; some of the projects were still ongoing. We observed that the number of river restoration projects has been increasing since 1989, which expresses society’s growing interest in improving the quality of aquatic environments. We revealed that 56% of these European river restoration projects have been implemented by dedicated entities and stakeholders, not as part of any structured, larger-scale river restoration policy; this indicates that most European countries do not have integrated plans for river restoration. Our analysis showed that 52% of the projects analyzed have been designed and implemented without the participation of local stakeholders. It also showed that the budgets for river restoration projects did not differ significantly across various time horizons from 1981 to 2016. In our study, the average cost of restoring 1 ha of an average European river was 310 000 EUR (or 195 000 EUR if 4 outlying values are excluded). Considering these projects’ permanent assets and including their amortization, for European river systems, we calculated the average unit price of a river restoration’s value in terms of ecosystem meta-service to be 7 757 EUR · ha^-1 · year^-1 (4 875 EUR · ha^-1 · year^-1 if 4 outlying values were excluded).

Vascular endothelial growth factors (VEGFs) are regarded as the key vasculogenic mediators in the human body, of which VEGF-A is considered as one of the important types. It is the major growth factor possessing increasing vascular permeability as well as angiogenesis properties. This article provides intuition into the different effects of VEGF-A on vessels in ovarian hyper stimulation syndrome (OHSS) which is defined as a complication of in-vitro fertilization for infertility treatment, and obesity, a universally health problem. These are two dissimilar pathways outcomes for VEGF-A effects, thereby This article reviews updated information about the mentioned disorders and reveals that how this kind of growth factor yields different results on vasculature by activating distinct intracellular signaling in endothelial cells.

This paper reports a new global Mittag-Leffler synchronization criterion with regard to fractional-order hyper-chaotic financial systems by designing the suitable impulsive control and the state feedback controller. The significance of this impulsive synchronization lies in the fact that the backward economic system can synchronize asymptotically with the advanced economic system under the effective impulse macroeconomic management means. Matlab LMI-toolbox is utilized to deduce the feasible solution in numerical example, which shows the effectiveness of the proposed methods. It is worth mentioning that the LMI-based criterion usually requires the activation function of the system to be Lipschitz, but the activation function in this paper is fixed and truly nonlinear, which cannot be assumed to be Lipschitz continuous. This is another mathematical difficulty overcome in this paper.

The Internet of Things (IoT) is revolutionizing manufacturing, serving as a digital makeover tool that collects essential production data. The transition to IoT generates vast data volumes, requiring robust storage and system architecture. Cloud computing steps in here, accommodating these needs with ease. In this setup, a network of sensors across a manufacturing unit captures valuable data, which is transmitted to the cloud. Specialized software then transforms this data into operational performance insights. This digital integration can elevate a manufacturing business, coalescing with existing systems to deliver remarkable results. As the IoT gains popularity, digital manufacturing is poised to soar. However, security has become a paramount concern, especially for retrofitted IoT devices operating with limited human intervention. Researchers have proposed various solutions for IoT security issues. In this study, we conduct a literature review (LR) to identify the security requirements for retrofitting IoT devices in manufacturing. Our goal is to provide a straightforward roadmap for industry stakeholders, highlighting the most suitable security solutions for their retrofitted IoT systems. Our review focuses on scientific papers from the past four years, specifically addressing the security and simplicity of retrofitted IoT architecture in manufacturing. The overarching aim is to define the security needs for IoT retrofitted devices in manufacturing, facilitating the smooth integration of process data into cloud platforms.

Microwave plasma-driven nitrogen fixation can occur at atmospheric pressure without greenhouse gas emissions. However, this method still faces the challenge of high energy consumption and low production. Plasma-catalyst combined systems are widely used to increase production and reduce energy consumption in nitrogen fixation. However, the efficacy of current catalysts remains limited. In this paper, the metal-organic framework materials CuBTC and ZIF-8 are combined with the atmospheric microwave plasma for nitrogen fixation. The experimental results show their better catalytic effect than the ordinary catalyst ZSM-5. The maximum nitrogen oxide concentration reaches 33400 ppm, and the lowest energy consumption is 2.05 MJ/mol. Compared to no catalyst, the nitrogen oxide production can be increased by 17.1%, and the energy consumption can be reduced by 14.6%. The transmission electron microscopy (TEM) images of the catalysts reveal obvious differences in the catalyst particle sizes. Notably, catalysts with smaller particle sizes attain higher NOx concentrations compared to their larger counterparts. To the knowledge of the authors, this is the first effort to study the synergistic effects of atmospheric microwave plasma and metal-organic framework materials on nitrogen fixation.

Driven by global sustainability trends, Building Information Modelling (BIM) technology is in-creasingly becoming a key tool in the construction industry to improve efficiency and sustaina-bility.This study aims to identify the key factors affecting BIM implementation in the context of Environmental, Social and Governance (ESG) and Sustainable Development Goals (SDGs) and to construct a theoretical framework for BIM implementation based on these factors.To achieve this objective, this study used a systematic literature review (SLR) method to systematically review the relevant literature between 2009 and 2024, and identified 16 key factors from the selected 406 studies through keyword co-occurrence analysis (using VOSviewer) and data coding.These key factors include:Top Management Support for ESG and SDGs,Alignment of SDGs,ESG Integra-tion,Technical Support,BIM Software,BIM Hardware,Structural Adjustment & Collabora-tion,Capacity Building,Change Management,Skill and Attitude,Educational Training and Devel-opment,Incentive Mechanism,Roles & Responsibilities,Sustainable Construction Practic-es,Policies and Regulations,Resource Efficiency.This study categorises these factors under the STOPE framework (Strategy, Technology, Organisation, People, Environment) and proposes a theoretical implementation framework for BIM accordingly. The findings not only provide a practical guiding framework for the sustainable development of construction companies in the context of ESG and SDG integration, but also lay a solid theoretical foundation for future empiri-cal research.

Privacy protection is a fundamental guarantee for secure data flows and serve as a basic requirement for data security. A reasonable privacy protection system acts as a catalyst for unlocking the financial value of data. The current legislative framework for privacy protection in data flows in China, adhering to the principle of proportionality, establishes the “informed-consent” rule for data collection and processing, data classification and grading management measures, and remedies for data leakage and other risks. In addition, in judicial practice, typical disputes regarding personal information protection and privacy rights have promoted to clarify the scope of collecting user personal information and biometric data. Despite ongoing improvements needed in legislative, judicial and technical approaches to data privacy protection, China’s commitment and practice in privacy protection of data flows are noteworthy. The existing legislation, law enforcement and technical practices are playing an increasingly vital role in realizing the financial value of data and are essential for international cooperation on data privacy protection. Furthermore, it is crucial to actively explore cooperation mechanism for cross-border data flows under the principle of data sovereignty, participate in developing international rules for cross-border data flows, and formulate different management norms for cross-border circulation of data in different industries.

The objective of image super-resolution is to reconstruct a high-resolution (HR) image with the prior knowledge from one or several low-resolution (LR) images. However, in the real world, due to the limited complementary information, the performance of both single-frame and multi-frame super-resolution reconstruction degrades rapidly as the magnification increases. In this paper, we propose a novel two-step image super resolution method concatenating multi-frame super-resolution (MFSR) with single-frame super-resolution (SFSR), to progressively upsample images to the desired resolution. The proposed method consisting of an L0-norm constrained reconstruction scheme and an enhanced residual back-projection network, integrating the flexibility of the variational model-based method and the feature learning capacity of the deep learning-based method. To verify the effectiveness of the proposed algorithm, extensive experiments with both simulated and real world sequences were implemented. The experimental results show that the proposed method yields superior performance in both objective and perceptual quality measurements, compared to the baseline super-resolution algorithms in the cascade model. In addition, the experiment indicates that this cascade model can be robustly applied to different SFSR and MFSR methods.

Metal-organic frameworks (MOFs) are being investigated for their potential to develop high-performance supercapacitors due to their porosity and well-defined topologies. This work presents a new hybrid nanocomposite, developed through a solvothermal method, using bimetallic (Fe/Ni) MOF and polyaniline. The morphology and structure of the electrode material were evaluated using scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR). Electrochemical performances were evaluated in 6 M KOH using an electrochemical workstation. The specific capacitance of Bimetallic Fe/Ni MOF and Bimetallic Fe/Ni MOF with PANI at 10 mVs-1 are 104 Fg-1 and 90 Fg-1, respectively. The EIS spectra show that the composite has lower series resistance and higher charge transfer resistance compared to pristine Fe/Ni MOF.

Reducing the immunogenicity of animal-derived monoclonal antibodies (mAbs) for use in humans is critical to maximize therapeutic effectiveness and preclude potential adverse events. While traditional humanization methods have primarily focused on grafting antibody Complementarity-Determining Regions (CDRs) on homologous human antibody scaffolds, framework regions can also play essential roles in antigen binding. Here, we describe the humanization of the pan-HLA-DR mAb 44H10, a murine antibody displaying significant involvement of the framework region in antigen binding. Using a structure-guided approach, we identify and restore framework residues that directly interact with the antigen or indirectly modulate antigen binding by shaping the antibody paratope and engineer a humanized antibody with affinity, biophysical profile, and molecular binding basis comparable to that of the parental 44H10 mAb. As a humanized molecule, this antibody holds promise as a scaffold for the development of MHC class II-targeting therapeutics and vaccines.

Nanoporous materials, such as Metal-Organic Frameworks (MOFs), are renowned for their high selectivity as gas adsorbents due to their specific surface area, nanoporosity, and active surface chemistry. A significant challenge for their widespread application is their behavior and reduced gas uptake in wet conditions, attributed to competitive adsorption between gas and water. The current understanding of gas adsorption in wet materials is constrained by studies that utilize only small amounts of powdered porous materials (in the milligram range) within very small reactors (1-5 mL). This leaves a significant gap in knowledge about gas adsorption behaviors in larger reactors and when MOF sample sizes are increased (to the gram scale) for scaling up the process. Additionally, there is a notable dearth of experimental research in situations where MOFs are heavily saturated with water. To address these gaps, this study measures CH4 adsorption in MOFs under conditions of high moisture content, using larger samples of MOFs (in grams) and a large volume reactor. We selected commercially available MOFs, including HKUST-1, ZIF-8, MOF-303, and activated carbon. A high-pressure isotherms (at T = 274.15 K) using volumetric approach were measured to qualitatively compare the moles of gas adsorbed under both dry and wet conditions across different MOFs and weights. Experimental results show that the presence of water led to a decrease in total CH4 mmol adsorption in MOFs, with a more significant decrease observed in hydrophilic MOFs compared to hydrophobic ones. For hydrophilic MOFs, presence of crossover isotherms at crossover pressure provided evidence of water's conversion to hydrate and its positive role in enhancing total gas uptake, which varied among different hydrophilic MOFs. Oversaturated MOFs showed a larger deviation between dry and wet cases at higher pressures. The high-pressure isotherm data confirm the positive role of hydrophilic MOFs in the conversion of water to hydrate in oversaturated MOFs when MOFs sample size is large.

The World Health Organization (WHO) has released reports indicating that heart disorders hold the unfortunate distinction of being the primary cause of death worldwide. Shockingly, an astonishing estimated 17.9 million lives are claimed by heart diseases annually, accounting for an alarming 31% of all global deaths. With all the flaws in the clinical situation, it is frequently challenging to assess the severity of cardiac disease and forecast its course of progression due to heterogeneity and complex interplay of factors. Therefore, early heart disease detection is essential for effective therapy. To address these challenges, Machine Learning (ML) boosting algorithms play a pivotal role as the main components of predictive analytics required to do this. The main objective of this study is to develop a comprehensive comparative framework to predict heart diseases using state-of-the-art machine learning with boosting techniques such as Decision Tree, Random Forest, Gradient Boosting, Catboost, XGboost, Light GBM, and Adaboost. To evaluate the performance of the models, a large heart disorders dataset is used from the UCI machine learning repository, comprised of 26 feature-based numerical and categorical attributes with 8763 samples from all over the globe. The Experimental results reveal that AdaBoost attained the highest accuracy of 95% and outperforms other algorithms concerning various performance measures like precision= 0.98, recall= 0.95, specificity= 0.95, f1-score=0.01, Negative predicted value= 0.83, False positive rate= 0.04, False negative rate= 0.04 and False Development rate= 0.01.

Most research findings in Ghana and all over the globe lay much emphasis on the technical knowledge of plastics; especially giving preference to the biodegradable over the synthetic/Single-Use plastics. Questionnaires were administered to about 200 respondents in the Ho Municipality out of which 163 responses were filled and returned. Results showed that of all the variables tested only Legislative Framework has a meaningful impact on the behaviour of Ho indigenes. Awareness level: (Synthetic Plastics and Biodegradable Plastics) as well as Attitudinal Change do not influence people’s behaviour when using plastics. Findings from this study are further discussed and recommendations were proffered to instil eco-friendly behaviour among people.

Today, the world is undergoing a major investment cycle in infrastructure, which is vital for the development and prosperity of countries and society. Management deficiencies in infrastructure projects are well known and some of them concerns weak engagement of project stakeholders. Importance of stakeholder engagement as the key success factors for infrastructure projects is recognized. However, the literature gives very few answers how this process manifests in projects. Some scholars concluded that it is a complex and undertheorized management process which necessitates more empirical research. The research questions seek to identify how practitioners perceive the stakeholder engagement, who implements activities and processes related to engagement and in what way, which organizational aspects influence the process and how success is achieved in infrastructural projects. Interviews were conducted with eight experienced experts who participated in a whole series of (complex) infrastructure projects. The interviews examined all aspects of the engagement of stakeholders in detail, and a thematic analysis was carried out. Based on the analysis, success/failure factors related to the stakeholder engagement process were defined. As a result, a framework model for engaging stakeholders and achieving success in infrastructure projects is presented, which is based on three management levels and three levels of project success.

Zeolitic imidazolate frameworks (ZIFs) have potential for various gas and ion separations due to their well-defined pore structure and relatively easy fabrication process compared to other metal-organic frameworks. As a result, many reports have focused on preparing polycrystalline and continuous ZIF layers on porous supports. In this study, we investigated how humidity and chamber temperature influence the structure of a ZIF-8 layer prepared by the hydrothermal method. We controlled the chamber temperature (ranging from 50 °C to 70 °C) and relative humidity (ranging from 20 % to 100 %) using a thermos-hygrostat chamber. We found that as the chamber temperature increased, ZIF-8 preferentially grew into particles rather than forming a continuous polycrystalline layer. By measuring the temperature of the reacting solution based on chamber humidity, we discovered that the heating rate of the reacting solution varied with humidity, even at the same chamber temperature. At higher humidity, thermal energy transfer was accelerated as water vapor delivered more energy to the reacting solution. Therefore, a continuous ZIF-8 layer could be formed more easily at low humidity ranges (ranging from 20 % to 40 %), while micron ZIF-8 particles were synthesized at a high heating rate. Our findings suggest that controlling the heating rate of the reacting solution is critical to preparing a continuous ZIF-8 layer, particularly for future scale-up of ZIF-8 membranes.

The National Occupational Competency Profile (NOCP) – the competency framework for paramedics in Canada – is presently undergoing revision. Since the NOCP was published in 2011, paramedic practice, healthcare and society have changed dramatically. To inform the revision, we sought to identify emerging concepts in the literature that would inform the development of competencies for paramedics. We conducted a restricted literature review and content analysis of all published and grey literature pertaining to or informing Canadian paramedicine from 2011 to 2022. Three authors performed a title and abstract, and full-text review to identify and label concepts informed by existing findings. A total of 302 articles were categorized into eleven emerging concepts related to competencies: Inclusion, Diversity, Equity, and Accessibility (IDEA) in paramedicine; Social responsiveness, justice, equity and access; Anti-racism; Healthy Professionals; Evidence Informed Practice and Systems; Complex Adaptive Systems; Learning Environment; Virtual Care; Clinical Reasoning; Adaptive Expertise; and Planetary Health. This review identified emerging concepts to inform the development of the 2023 National Occupational Standard for Paramedics (NOSP). These concepts will inform data analysis, development group discussions, and competency identification.

Water resource management and flood forecasting are crucial societal and financial stakes requiring reliable predictions of flow parameters (depth, velocity), the accuracy of which is often limited by uncertainties in hydrodynamic numerical models. In this study, we assess the effect of two uncertainty sources, namely breach characteristics induced by overtopping and the roughness coefficient, on water elevations and inundation extent. A two-dimensional (2D) hydraulic solver was applied in a Monte Carlo integration framework to a reach of the Loire river (France) including about 300 physical parameters. Inundation hazard maps for different flood scenarios allowed highlighting the impact of the breach development chronology. A special attention was paid to proposing a relevant sensitivity analysis to exam the factors influencing the depth and extent of flooding. The spatial analysis of vulnerability area induced by a levee breach width exhibits that, with increasing the flood discharge, the rise of the parameter influence is accompanied by a more localized spatial effect. This argues for a local analysis to allow a clear understanding of the flood hazard. The physical interpretation, highlighted by a global sensitivity analysis, showed precisely the dependence of the flood simulation on the main factors studied, i.e. the roughness coefficients and the characteristics of the breaches.

The integration of metal nanoparticles and solid carriers can achieve ideal stability, high load and good conductivity. In this work, copper nanoparticles (Cu NPs) were sequentially deposited on a cobalt metal-organic framework (Co-MOF) by bonding with exposed imino groups, followed by a reduction reaction to prepare a new Cu@Co-MOF composite. Cu@Co-MOF acts as a non-enzymatic electrochemical sensor to detect glucose (Glu) in an alkaline medium. The composite working electrode of Cu@Co-MOF/GCE (GCE = glassy carbon electrode) improves the electrocatalytic activity for Glu oxidation. Cu@Co-MOF/GCE shows excellent electrocatalytic performances in Glu concentration ranging 0.005~1.8 mmol∙L⁻¹ (mM): the sensitivities are 282.89 μA∙mM⁻¹∙cm⁻² in 0.005-0.4 mM Glu and 113.15 μA∙mM⁻¹∙cm⁻² in 0.4-1.8 mM Glu respectively with low detection limit of 1.6 μM (S/N = 3) and high selectivity and stability.

We report that ethanol, used together with water, plays a crucial role in tuning the structures of a zirconium-based Metal-Organic Framework, the 12-connected MOF-801, and the possible mechanisms of this modulating effect. By employing the cosolvent system of ethanol and water just under room temperature without the presence of a monotopic carboxylic acid as the modulator, MOF-801 in various morphologies of different sizes can be synthesized. The linear correlation between the ethanol/water ratio and the crystal sizes is also demonstrated. The growth mechanism is mainly explained by ethanol’s binding with the metal ion clusters and the Marangoni Flow Effect. Ethanol competes with the linker molecules in coordinating with the Zr metal clusters, a role similar to that of the modulators. The Marangoni Flow Effect, which dominates at a certain solvent ratio, further promotes the 1-D alignment of the MOF-801 crystals.

This article discusses the implementation of the Water Framework Directive (WFD) in the Netherlands and intends to show how law and politics combine in river basin management. Initially, the implementation of the WFD in the Netherlands was approached as a technical and administrative issue, handled by water quality and ecology experts, but in 2003 this approach was broken open by the agricultural sector, who were worried about stricter regulation. Subsequently, the environmental objectives of the WFD were set as low as possible and they are not used in authorising individual projects. Yet, in 2015 of the European Court of Justice determined that the environmental objectives have binding effect and that Member States have to refuse authorisation for projects that endanger the achievement of these objectives. In the future, the European Court of Justice and national courts may force the Dutch government to change its approach. They may only do so because and as long as they enjoy sufficient social and political support and function relatively independently. The article concludes that, to understand river basin management fully, one has to open up the black box of water law and become a kind of water lawyer.

Measuring the status and trends of biodiversity is critical for making informed decisions about the conservation, management or restoration of species, habitats and ecosystems. Defining the reference state against which status and change are measured is essential. Typically, reference states describe historical conditions, yet historical conditions are challenging to quantify, may be difficult to falsify, and may no longer be an attainable target in a contemporary ecosystem. We have constructed a conceptual framework to help inform thinking and discussion around the philosophical underpinnings of reference states and guide their application. We characterise currently recognised historical reference states and describe them as Pre-Human, Indigenous Cultural, Pre-Intensification and Hybrid-Historical. We extend the conceptual framework to include contemporary reference states as an alternative theoretical perspective. The contemporary reference state framework is a major conceptual shift that focuses on current ecological patterns and identifies areas with higher biodiversity values, regardless of the disturbance history. The specific context for which we design the contemporary conceptual frame is underpinned by an overarching goal—to maximise biodiversity conservation and restoration outcomes in existing ecosystems. The contemporary reference state framework can account for the inherent differences in the diversity of biodiversity values (e.g., species richness, habitat complexity) across spatial scales, communities and ecosystems. In contrast to historical reference states, contemporary references states are measurable and falsifiable. This ‘road map of reference states’ offers perspective needed to define and assess the status and trends in biodiversity and habitats. Our framework for contemporary reference states provides a tractable way for policy-makers and practitioners to navigate biodiversity assessments to maximise conservation and restoration outcomes in contemporary ecosystems. We illustrate how to define a contemporary reference state using an example from south-eastern Australia.

This research article reports the results of a qualitative case study that correlates academic literature with five Industry 4.0 cyber trends, seven cyber risk frameworks and two cyber risk models. While there is a strong interest in industry and academia to standardise existing cyber risk frameworks, models and methodologies, an attempt to combine these approaches has not been done until present. We apply the grounded theory approach to derive with integration criteria for the reviewed frameworks, models and methodologies. Then, we propose a new architecture for the integration of the reviewed frameworks, models and methodologies. We therefore advance the efforts of integrating standards and governance into Industry 4.0 and offer a better understanding of a holistic economic impact assessment model for IoT cyber risk.

Multi-object tracking (MOT) in airborne videos is a challenging problem due to the uncertain airborne vehicle motion, vibrations of the mounted camera, unreliable detections, size, appearance and motion of the moving objects as well as occlusions due to the interaction between the moving objects and with other static objects in the scene.To deal with these problems, this work proposes a four-stage Hierarchical Association framework for multiple object Tracking in Airborne video (HATA). The proposed framework combines data association-based tracking (DAT) methods and target tracking using a Compressive Tracking approach, to robustly track objects in complex airborne surveillance scenes. In each association stage, different sets of tracklets and detections are associated to efficiently handle local tracklet generation, local trajectory construction, global drifting tracklet correction and global fragmented tracklet linking. Experiments with challenging airborne video datasets show significant tracking improvement compared to existing state-of-art methods.

The concept of ‘big data’ has been widely discussed, and its value has been illuminated throughout a variety of domains. To quickly mine potential values and alleviate the ever-increasing volume of information, machine learning is playing an increasingly important role and faces more challenges than ever. Because few studies exist regarding how to modify machine learning techniques to accommodate big data environments, we provide a comprehensive overview of the history of the evolution of big data, the foundations of machine learning, and the bottlenecks and trends of machine learning in the big data era. More specifically, based on learning principals, we discuss regularization to enhance generalization. The challenges of quality in big data are reduced to the curse of dimensionality, class imbalances, concept drift and label noise, and the underlying reasons and mainstream methodologies to address these challenges are introduced. Learning model development has been driven by domain specifics, dataset complexities, and the presence or absence of human involvement. In this paper, we propose a robust learning paradigm by aggregating the aforementioned factors. Over the next few decades, we believe that these perspectives will lead to novel ideas and encourage more studies aimed at incorporating knowledge and establishing data-driven learning systems that involve both data quality considerations and human interactions.

Full waveform (FW) LiDAR holds great potential for retrieving vegetation structure parameters at a high level of detail, but this prospect is constrained by practical factors such as lack of available handy processing tools and technical intricacy of waveform processing. This study introduces a new product, named the Hyper Point Cloud (HPC) derived from FW LiDAR data, and explore its potential applications such as tree crown delineation using the HPC-based intensity and percentile height (PH) surfaces, which show a promising solution to the constraints of using FW LiDAR data. Results of the HPC present a new direction to handle FW LiDAR data and offer prospects for studying the mid-story and understory of vegetation with high point density (~ 182 points/m2). The intensity-derived digital surface model (DSM) generated from the HPC shows that the ground region has larger maximum intensity (MAXI) and mean intensity (MI) than the vegetation region while having smaller total intensity (TI) and number of intensities (NI) at the given grid cell. Our analysis of intensity distribution contours at individual tree level exhibit similar patterns, indicating that the MAXI and MI are decreasing from the tree crown center to tree boundary while a rising trend is observed for TI and NI. These intensity variable contours provide a theoretical justification for using HPC-based intensity surfaces to segment tree crowns and exploit their potential for extracting tree attributes. The HPC-based intensity surfaces and the HPC-based PH Canopy Height Models (CHM) demonstrate promising tree segmentation results comparable to the LiDAR derived CHM for estimating tree attributes such as tree locations, crown widths and tree heights. We envision that products such as the HPC and the HPC-based intensity and height surfaces introduced in this study can open new perspectives to use FW LiDAR data and alleviate the technical barrier of exploring FW LiDAR data for detailed vegetation structure characterization.

Hyper-radiosensitivity (HRS) is the increased sensitivity to low doses of ionizing radiation observed in most cell lines. We previously demonstrated that HRS is permanently abolished in cells irradiated at a low dose rate (LDR), in a mechanism dependent on transforming growth factor β3 (TGF-β3). In this study, we aimed to elucidate the activation and receptor binding of TGF-β3 in this mechanism. T-47D cells were pre-treated with inhibitors of potential receptors and activators of TGF-β3, along with addition of small extracellular vesicles (sEVs) from LDR primed cells, before their radiosensitivity was assessed by the clonogenic assay. The protein content of sEVs from LDR primed cells was analyzed with mass spectrometry. Our results show that sEVs contain TGF-β3 regardless of priming status, but only sEVs from LDR primed cells remove HRS in reporter cells. Inhibition of the matrix metalloproteinase (MMP) family prevents removal of HRS, suggesting an MMP-dependent activation of TGF-β3 in the LDR primed cells. We demonstrate a functional interaction between TGF-β3 and activin receptor like kinase 1 (ALK1), by showing that TGF-β3 removes HRS through ALK1 binding, independent of ALK5 and TGF-βRII. These results are an important contribution to a more comprehensive understanding of the mechanism behind TGF-β3 mediated removal of HRS.

The diverse, heterogeneous, and dynamic nature of urban data poses challenges for semantic integration and knowledge sharing in smart city applications. Traditional urban governance sectors often develop domain-specific data standards, which are insufficient for integrating and applying multi-source heterogeneous data. This article proposes the Smart City Ontology Framework (SMOF), leveraging ontology technology for semantic-level data sharing and interoperability across different industries. SMOF adopts a top-down integration strategy, harmonizing diverse data standards from IoT, BIM, and GIS into a cohesive structure. This research delineates the foundational components of SMOF, encompassing entities, relationships, properties, events, and rules, and outlines the methodology and principles underpinning its construction. We further develop a scalable, hierarchical classification system for entities, properties and relationships, adaptable for specific semantic applications. Through a fire emergency scenario, we exemplify SMOF's practical utility in knowledge representation and data mapping, highlighting its capacity for semantic querying within complex urban data ecosystems. Our findings indicate SMOF's significant role in fostering integrated urban data management, thereby enhancing data interoperability and applicability in diverse urban governance contexts.

The increasing complexity of IP network infrastructures necessitates efficient and adaptive configuration management solutions. This paper presents an AI-driven framework that integrates machine learning and reinforcement learning techniques to enhance the management of configurations for IP network devices. The proposed approach leverages a comprehensive dataset comprising configuration parameters and performance metrics to train predictive models. Experimental results demonstrate that the framework achieves an accuracy of 88% in predicting optimal configurations, significantly outperforming traditional methods. Additionally, the framework exhibits an average response time of 150 milliseconds for applying configuration changes, underscoring its efficiency. A reinforcement learning agent is implemented to adapt to dynamic network conditions, yielding improved decision- making over time. The user interface designed for the framework facilitates real-time monitoring and visualization of network configurations. The findings suggest that the AI- driven framework not only streamlines configuration management processes but also empowers network administrators to proactively address network challenges. Future work will focus on scalability, integration with emerging technologies, and user feedback mechanisms to further enhance the framework's effectiveness.

The failure of immunotherapies in cancer patients is being widely studied due to the complexities present in the tumor microenvironment (TME), where regulatory T cells (Treg) appear to actively participate in providing an immune escape mechanism for tumors. Therefore, therapies to specifically inhibit tumor-infiltrating Treg represent a challenge because Treg are distributed throughout the body and provide physiological immune homeostasis to prevent autoimmune diseases. Characterization of immunological and functional profiles could help to identify the mechanisms that need to be inhibited or activated to ensure Treg modulation in the tumor. To address this, quantitative in-silico approaches based on mechanistic mathematical models integrating multi-scale information from immune and tumor cells and the effect of different therapies have allowed the building of computational frameworks to simulate different hypotheses, some of which have subsequently been experimentally validated. Therefore, this review presents a list of diverse computational mathematical models that examine the role of Treg as a crucial immune resistance mechanism contributing to the failure of immunotherapy. In addition, this review highlights the relevance of certain molecules expressed in Treg that are associated with the TME immunosuppression, which could be incorporated into the mathematical model for a better understanding of the contribution of Treg modulation. Finally, different pre-clinical and clinical combinations of molecules are also included to show the trend of new therapies targeting Treg.

Interoperability has become crucial in the world of electronic health records (EHR), allowing for seamless data exchange and integration across diverse settings. It facilitates the integration of disparate systems, ensures that patient records are accessible, and enhances the care delivery process. The current interoperability landscape for electronic health records is saddled with challenges hindering efficient interoperability. Existing interoperability frameworks for EHR have failed to properly address many of these challenges relating to data exchange, security and privacy. To address these challenges, the TASSIPS framework is proposed as a comprehensive approach to achieving efficient interoperability. TASSIPS integrates robust security and privacy measures, providing real-time access to electronic health records that enable precise diagnoses, timely treatment plans and improved patient outcomes. TASSIPS offers a holistic and effective solution to healthcare interoperability challenges. An evaluation of TASSIPS via comparison with exicting frameworks concluded that it is an advancement in framework design for interoperabil-ity of EHR.