The advent of 5G technology represents a transformative leap in the realms of retail marketing and supply chain operations, offering unprecedented enhancements and posing unique challenges. This study explores the multifaceted impacts of 5G, focusing on its influence on customer experiences, operational efficiency, and supply chain management. The integration of 5G enables advanced technologies such as augmented reality (AR) and virtual reality (VR), significantly enhancing the interactivity and personalization of the shopping experience. Real-time data collection and analysis, facilitated by 5G, allow for more precise inventory management and optimized store operations, leading to improved customer satisfaction and reduced operational costs. In the supply chain domain, 5G technology offers enhanced visibility and control through real-time tracking and environmental monitoring, which improves logistics efficiency and product quality management. Despite these advancements, the adoption of 5G technology involves considerable challenges, including high infrastructure costs, data security concerns, and global disparities in technology deployment. Businesses must navigate these obstacles while leveraging the benefits of 5G to achieve competitive advantage. The study also highlights the role of 5G in integrating with other advanced technologies, such as artificial intelligence (AI) and blockchain, to drive further innovation. Additionally, 5G supports sustainability efforts by optimizing energy use and reducing waste. Overall, the successful implementation of 5G requires strategic investment and adaptability, but it holds the potential to revolutionize retail and supply chain operations, enhancing efficiency, customer engagement, and sustainability in a rapidly evolving digital landscape.

In this paper, comprehensive datasets are presented to advance research on information security breaches. The datasets include data on disclosed information security breaches affecting S&P500 companies between 2020 and 2023, collected through manual search of the Internet. Overall, the datasets include 504 companies, with detailed information security breach and financial data available for 97 firms that experienced a disclosed information security breach. This document will describe the datasets in detail, explain the data collection procedure and shows the initial versions of the datasets.

Maritime data not only identify financial resources that can significantly enhance the efficiency of shipping companies but also play a pivotal role in promoting eco-friendly and sustainable shipping practices. However, active corporate strategies are required to include maritime data essential for the commercial operation of autonomous ships in the form of corporate assets by transparent evaluating the data. The aim of this study is to review previous studies on da-ta-valuation models, identify a valuation model suitable for maritime business, consider the characteristics of maritime data. The analytic hierarchy process (AHP) method is used to derive priorities for valuing maritime data assets, with emphasis placed on the characteristics of mari-time data, followed by the character of the future-oriented maritime-data market and the da-ta-valuation model. The results obtained that the most important factors in selecting a maritime data-valuation model by analyzing the AHP method were identified as the characteristics of the maritime data(M1). The next most important factors analyzed were the features of the mari-time-data market(M2) and the features of the maritime data-valuation model(M3) in order. In addition, the characteristics of maritime data were preferentially considered among the main criteria, and the market approach (A2) was selected as the most optimal valuation model. The potential impact of this implementation could contribute to the establishment of an intelligent technology market by estimating the value of data and developing a platform for maritime data trading, allowing for more efficient data sharing and utilization by maritime autonomous sur-face ship(MASS).

This study presents a novel approach to enhance Large Language Models (LLMs) like Alpaca by dynamically integrating real-time information. This method addresses the issue of content hallucination and data relevancy by automatically collecting and integrating current data from credible sources into model prompts. Experiments show a significant improvement in accuracy and a decrease in content hallucination, with a manageable increase in response time. The research underscores the potential of real-time data integration in making LLMs more accurate and contextually relevant, setting a foundation for future advancements in dynamic data processing in AI.

One of the most promising advancements in healthcare is the application of digital twin technology. Digital twins are virtual replicas of real-world human patients and can be used for monitoring, and diagnosis, and as a tool to design treatment strategies tailored to individual patients. Furthermore, digital twins could also be helpful in finding novel treatment targets and predicting the effects of drugs and other chemical substances in development. This review article delves into the various data sources and methodologies that contribute to the construction of digital twins. Each data source, including blood glucose levels, heart MRI and CT scans, cardiac electrophysiology, written reports, and multi-omics data, comes with different challenges regarding standardization, integration, and interpretation. We showcase how various datasets and methods are used to overcome these obstacles and generate a digital twin. While digital twin technology has seen significant progress, there are still hurdles in the way to achieving a fully comprehensive patient digital twin. Developments in non-invasive and high-throughput data collection, as well as advancements in modeling and computational power will be crucial to improve digital twin systems. We discuss a few critical developments in light of the current state of digital twin technology. Despite challenges, digital twin research holds great promise for personalized patient care and has the potential to shape the future of healthcare innovation.

Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Examples of AI techniques are machine learning, neural networks and deep learning. AI can be applied in many different areas, such as econometrics, biometry, e-commerce and the automotive industry. In recent years, AI has found its way into healthcare as well, helping doctors to make better decisions (‘clinical decision support’), localizing tumors in magnetic resonance images, reading and analyzing reports written by radiologists and pathologists, and much more. However, AI has one big risk: it can be perceived as a ‘black box’, limiting trust in its reliability, which is a very big issue in an area in which a decision can mean life or death. As a result, the term Explainable Artificial Intelligence (XAI) has been gaining momentum. XAI tries to ensure that AI algorithms (and the resulting decisions) can be understood by humans. In this narrative review, we will have a look at the current status of XAI in healthcare, describe several issues around XAI, and discuss whether it can really help healthcare to advance, for example by increasing understanding and trust. Finally, alternatives to increase trust in AI are discussed, as well as future research possibilities in the area of XAI.

Cities are undergoing huge shifts in technology and operations in recent days, and `data science' is driving the change in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting insights or actionable knowledge from city data and building a corresponding data-driven model is the key to making a city system automated and intelligent. Data science is typically the study and analysis of actual happenings with historical data using a variety of scientific methodology, machine learning techniques, processes, and systems. In this paper, we concentrate on and explore ``Smart City Data Science", where city data collected from various sources like sensors and Internet-connected devices, is being mined for insights and hidden correlations to enhance decision-making processes and deliver better and more intelligent services to citizens. To achieve this goal, various machine learning analytical modeling can be employed to provide deeper knowledge about city data, which makes the computing process more actionable and intelligent in various real-world services of today's cities. Finally, we identify and highlight ten open research issues for future development and research in the context of data-driven smart cities. Overall, we aim to provide an insight into smart city data science conceptualization on a broad scale, which can be used as a reference guide for the researchers, professionals, as well as policy-makers of a country, particularly, from the technological point of view.

Precision medicine emphasizes fine-grained diagnostics, taking individual variability into account to enhance treatment effectiveness. Parkinson's Disease (PD) heterogeneity among individuals is a proof that disease subtypes exist, and assigning individuals to subgroups is necessary for a better understanding of disease mechanisms and designing precise treatment approaches. The purpose of this study was to identify PD subtypes using RNA-Seq data in a combined pipeline including unsupervised machine learning, bioinformatics, and network analysis. 210 post mortem brain RNA-Seq samples from PD (n = 115) and Normal Controls (NC, n = 95) were obtained with a systematic data retrieval following PRISMA statements and a fully data-driven clustering pipeline was performed to identify PD subtypes. Bioinformatics and Network analyses were performed to characterize the disease mechanisms of the identified PD subtypes and to identify target genes for drug repurposing. Two PD clusters were identified and 42 DEGs were found (p.adjusted ≤ 0.01). PD clusters had significantly different gene network structures (p < 0.0001) and phenotype-specific disease mechanisms, highlighting the differential involvement of the Wnt/β-catenin pathway regulating adult neurogenesis. NEUROD1 was identified as a key regulator of gene networks and ISX9 and PD98059 were identified as NEUROD1-interacting compounds with disease-modifying potential, reducing the effects of dopaminergic neurodegeneration. This hybrid data analysis approach could enable precision medicine applications by providing insights for the identification and characterization of pathological subtypes. This workflow has proven useful on PD brain RNA-Seq, but its application to other neurodegenerative diseases is encouraged.

In the current age of the Fourth Industrial Revolution ($4IR$ or Industry $4.0$), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding real-world applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study's key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world applications areas, such as cybersecurity, smart cities, healthcare, business, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for not only the application developers but also the decision-makers and researchers in various real-world application areas, particularly from the technical point of view.

This article proposes the use of a multi-scale and multi-sensor approach to collect and modelling 3D data concerning wide and complex areas in order to obtain a variety of metric information in the same 3D archive, based on a single coordinate system. The employment of these 3D georeferenced products is multifaceted and the fusion or integration among different sensors data, scales and resolutions is promising and could be useful for the generation of a model that could be defined as hybrid. The correct geometry, accuracy, radiometry and weight of the data models are hereby evaluated comparing integrated processes and results from Terrestrial Laser Scanner (TLS), Mobile Mapping System (MMS), Unmanned Aerial Vehicle (UAV), terrestrial photogrammetry, using Total Station (TS) and Global Navigation Satellite System (GNSS) as topographic survey. The entire analysis underlines the potentiality of the integration and fusion of different solutions and is a crucial part of the “Torino 1911” project whose main purpose is mapping and virtually reconstructing the 1911 Great Exhibition settled in the Valentino Park in Turin (Italy).

Environmental policy involving citizen science (CS) is of growing interest. In support of this open data stream, validation or quality assessment of the CS data and their appropriate usage for evidence-based policy making, needs a flexible and easily adaptable data curation process ensuring transparency. Addressing these needs, this paper describes an approach for automatic quality assurance as proposed by the Citizen OBservatory WEB (COBWEB) FP7 project. This approach is based upon a workflow composition that combines different quality controls, each belonging to seven categories or ‘pillars’. Each pillar focuses on a specific dimension in the types of reasoning algorithms for CS data qualification. These pillars attribute values to a range of quality elements belonging to three complementary quality models. Additional data from various sources, such as Earth Observation (EO) data, are often included as part of the inputs of quality controls within the pillars. However, qualified CS data can also contribute to the validation of EO data. Therefore, the question of validation can be considered as ‘two sides of the same coin’. Based on an invasive species CS study, concerning Fallopia japonica (Japanese knotweed), the paper discusses the flexibility and usefulness of qualifying CS data, either when using an EO data for the validation within the quality assurance process, or validating an EO data product that describes the risk of occurrence of the plant. Both validation paths are found to be improved by quality assurance of the CS data. Addressing the reliability of CS open data, issues and limitations of the role of quality assurance for validation, due to the quality of secondary data used within the automatic workflow, are described, e.g. error propagation, paving the route to improvements in the approach.

Vibration suppression in a field of frequencies and the creation of band gaps which do not allow the propagation of the waves is studied in this paper by means of microstructures designed through topology optimization. Topology optimization is formulated in frequency domain. Band gap design is based on Floquet-Bloch theory and genetic optimization. The result is appealing in view of modern 3D printing techniques.

Neck circumference (NC), neck circumference to height ratio (NHtR) and neck circumference to weight ratio (NWtR) appear to be good candidates for the non-invasive management of non-alcoholic fatty liver disease (NAFLD). This study aimed to evaluate the ability of routine variables to assess and manage NAFLD in participants with obesity and NAFLD included in a 2-year nutritional intervention program. Anthropometric measurements, biochemical variables and imaging techniques were performed at different study time-points (baseline, 6, 12 and 24 months). The nutritional intervention significantly improved all anthropometric measurements as well as the glucose profile and the hepatic enzymes. NC and neck ratios combined with ALT levels and HOMA-IR showed good prediction ability for the hepatic fat content and hepatic steatosis at all the study time-points in a ROC analysis. The prediction ability of the combination panels improved when the weight loss variable was also considered. NC and neck ratios are easy anthropometric measurements that in combination with routine biochemical variables (ALT and HOMA-IR) showed good prediction ability of NAFLD. More research studies are necessary to validate the utility of these simple and easy variables as surrogate markers of NAFLD since their application could improve the prevention and management of this prevalent disease.

Data governance has emerged as a critical factor in determining the effectiveness and long-term stability of IT systems in today’s data-driven environment. It encompasses frameworks that guide organizations in managing data collection, storage, processing, usage, and sharing, while ensuring alignment with business objectives, compliance with regulations, and adherence to ethical standards. However, the complexities associated with data governance, particularly in the context of new technological models, pose significant challenges. This systematic review aims to explore the role of data governance in enhancing data quality, system stability, and regulatory compliance. It seeks to identify common issues, effective practices, and strategic implications for implementing data governance frameworks across diverse industries, focusing particularly on small and medium enterprises (SMEs). We conducted a systematic review of 68 eligible studies published between 2014 and 2024, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The included studies were assessed for risk of bias using the Cochrane Risk of Bias Assessment Tool. The review covered a range of study types, including qualitative (60%), quantitative (19%), and mixed-methods (21%) research, to provide a comprehensive understanding of data governance's impact on IT performance. The findings highlight that data governance significantly influences system success by improving data quality, operational efficiency, and regulatory compliance. Although qualitative studies predominantly emphasized in-depth analysis, a moderate representation of empirical validation through quantitative studies was observed. Common challenges identified include data migration issues, resistance to change, and budget constraints, particularly during system upgrades. Effective practices involve aligning data governance with business strategies, employing hybrid models, and regularly updating governance policies to adapt to technological advancements such as AI and big data. This review provides practical recommendations for IT managers and policymakers to enhance data governance frameworks. These include phased implementation approaches, the establishment of data governance committees, and the use of performance metrics to monitor data quality and system resilience. While significant progress has been made, further research is needed to address gaps related to emerging technologies and the unique challenges faced by SMEs in developing regions.

With many Americans eager to resume travel after prolonged restrictions from the coronavirus pandemic, car rentals are on the rise. According to Auto Rental News, “2021 U.S. Car Rental Revenue Climbs 21% Year-Over-Year” (Brown, 2021). In conjunction with increased car rental, and associated travel, we can expect to see an increase in cyber security and data privacy attacks. In the digital age, enterprises have a responsibility to protect consumers’ personally identifiable information (PII). As it pertains to rental vehicles, when consumers connect their mobile devices to a rental vehicle’s Entertainment and Infotainment System or utilize the built-in navigation feature, they do so, unbeknownst to themselves that the information from their devices is transferring to the vehicle, where it will remain, unless properly expunged from the system. A large majority of consumers are blissfully unaware that their PII is being stored, and how easy it would be for an adversary to potentially breach their data security and privacy. To expand upon this hypothesis, I conducted a further investigation on the subject, by partaking in the rental of three different vehicles, each belonging to a different manufacturer (Nissan, Chrysler, and Maserati), from three different vehicle rental companies (Enterprise, Hertz and Turo). I then, as a consumer, tested my hypothesis to determine if, I was able to easily breach the prior consumers’ data security and privacy by viewing devices that had previously connected to the vehicle, as well as any of the previous consumers’ personal information. The results were undeniable; the PII of the previous consumers is not protected.

The management of user data in the cloud is easily poised to become a giant issue for any business, with so much digital information floating around these days that open it up as an easy target when companies aren’t vigilant. In this paper, a novel cloud-based service is proposed that employs various advanced NLP and encryption methods with the use of blockchain. These techniques are combined to provide a solid solution to secure fundamental data such as credit card numbers, passports or any government identity cards etc. Using a hybrid NLP model, integrating Transformer Models and Named Entity Recognition (NER), to automatically catagorzing data as critical vs non-critical. Only the most important data is encrypted by a user’s cryptographic wallet before being divided into multiple chunks and stored on an exclusive cloud cluster; metadata then takes turns managing securely through blockchain to provide traceable means of retaining integrity. Smart contracts provide strict access control measures and change the cryptographic nonce if need be to prevent illegal entrance into a specific zone, thus create security. This all-inclusive strategy maintains well-known high security standards for protecting the confidentiality, availability and integrity of your sensitive data on a global scale—delivering you simple yet scalable secure world-class cloud-based data management. A proposed framework developed to fulfil the security requirements for current cloud services, which is a beneficial contribution in context of data protection and cloud Security.

The increasing request of digitized data among several fields, including the built environment and Cultural Heritage (CH) one points out the need of proficient ways to access, archive and share 3D data and related information among users. The sector of reality capture produces accurate and reliable products that can support building management and CH maintenance, at the price of heavy and resource-demanding data. An emerging solution to this problem is represented by the web platforms for 3D data management, that promise to relieve users from the costs of archive and hardware, providing effective visualization, access and sharing tools. The panorama of commercial web platforms is analyzed according to the Software as a Service business model and the features of some representative platforms are exposed. The paper discusses the main advantages in terms of diffused access and collaboration and the potential issues concerning the long-term archive and data persistence. The future perspectives of the web platform sector are promising as, according to the current development path, could be able to empower the built environment and CH sector with a diffused, systematic and aware use of 3D data.

The adoption of the Internet of Things (IoT) in supply chain operations has emerged as a pivotal advancement, offering the potential to significantly enhance operational efficiency, visibility, and responsiveness. This study explores the multifaceted impact of IoT integration in supply chains, emphasizing both the opportunities and challenges associated with its implementation. Through a combination of semi-structured interviews, case studies, and a comprehensive literature review, the research uncovers how IoT technologies contribute to improved asset tracking, real-time inventory management, and optimized logistics. Key benefits include enhanced operational efficiency, predictive maintenance capabilities, and better supply chain collaboration, which collectively lead to increased customer satisfaction and cost savings. However, the study also identifies several challenges, including the high costs of IoT infrastructure, difficulties in integrating new technologies with existing systems, and concerns over data security and privacy. These challenges pose significant barriers to adoption, particularly for smaller organizations. Despite these obstacles, the study highlights the substantial opportunities presented by IoT, such as fostering innovation, developing new business models, and enhancing supply chain resilience. The findings suggest that while the path to successful IoT adoption may be complex and resource-intensive, the potential benefits make it a valuable investment for organizations seeking to remain competitive in a rapidly evolving market. Addressing the identified challenges while leveraging the advantages of IoT will be crucial for companies aiming to optimize their supply chain operations and achieve long-term success.

In the context of modern healthcare, the concept of patient-centered care (PCC) has emerged as a crucial aspect, prompting the need for innovative frameworks to empower patients and enhance healthcare outcomes. This paper introduces the Patient-Oriented Medical Ontology Conceptual Framework (POMOCF), a powerful framework aimed at giving patients control over their personal and healthcare information. It prioritizes patient independence by allowing for direct control of electronic health records (EHRs) and facilitates the gathering of information through document upload or scanning. In the POMOCF front-end framework, we propose to maintain scalability, along with robust authorization, authentication, and secure data protocols, all of which are detailed within the framework. This approach enhances data accuracy and security, improving data management efficiency. As part of our ongoing development, we are planning to implement an innovative inference system that will automatically analyze and extract relevant details from the uploaded authorized document, provide real-time updates, and maintain data accuracy. This approach further strengthens data accuracy and prevents unauthorized modifications, thereby making the data management process more efficient. POMOCF leverages medical ontology principles to ensure standardized clinical concepts and improved semantic interoperability. Our integration plans involve integrating genotypic data for personalized medicine initiatives and strengthening the front-end infrastructure. By linking front-end interfaces with advanced technological solutions, we aim to provide effective healthcare delivery, promoting personalized care and enhancing patient outcomes.

Complexity science studies physical phenomena that cannot be explained by the mere analysis of the single units of a system but requires to account for their interactions. A feature of complexity in connected systems is the emergence of mesoscale patterns in a geometric space, such as groupings in bird flocks. These patterns are formed by groups of points that tend to separate from each other, creating mesoscale structures. When multidimensional data or complex networks are embedded in a geometric space, some mesoscale patterns can appear respectively as clusters or communities, and their geometric separability is a feature according to which the performance of an algorithm for network embedding can be evaluated. Here, we introduce a framework for the definition and measure of the geometric separability (linear and nonlinear) of mesoscale patterns by solving the travelling salesman problem (TSP), and we offer experimental evidence on embedding and visualization of multidimensional data or complex networks, which are generated artificially or are derived from real complex systems. For the first time in literature the TSP’s solution is used to define a criterion of nonlinear separability of points in a geometric space, hence redefining the separability problem in terms of the travelling salesman problem is an innovation which impacts both computer science and complexity theory.

The rescue, digitisation, quality control, preservation and utilization of long and high quality meteorological and climate records, particularly related to historical marine data, are crucial for advancing our understanding of the Earth's climate system. In combination with land and air measurements, the historical marine records serve as foundational pillars in linking present and past weather and climate information, offering essential insights into natural climate variability, extreme events in marine areas, baseline data for assessing current changes, and inputs for en-hancing predictive climate models and reanalyses. This paper provides an overview of marine data rescue activities covering the past centuries, presents and highlights several ongoing projects across the world and how the data are used in an integrative and international framework. Current and future continuous efforts in data rescue, digitisation, quality control and the de-velopment of temporally high resolution meteorological and climatological observations from the Sea, will greatly help to further complete our understanding and knowledge of the Earth system including extremes, as well as improving the quality of reanalysis.

The ability to monitor Sprained Ankle Rehabilitation Exercises (SPAREs) in home environments can help therapists to ascertain if exercises have been performed as prescribed. Whilst wearable devices have been shown to provide advantages such as high accuracy and precision during monitoring activities, disadvantages such as limited battery life, users' inability to remember to charge and wear the devices are often the challenges for their usage. Also, video cameras, which are notable for high frame rates and granularity, are not privacy-friendly. This paper, therefore, proposes the use and fusion of unobtrusive and privacy-friendly sensing solutions for data collection and processing during SPAREs in home environments. Two Infrared Thermopile Array (ITA-32) thermal sensors and two Frequency Modulated Continuous Wave (FMCW) Radar sensors were used to simultaneously monitor 15 healthy participants during SPAREs which involved twisting their ankle in 4-fundamental movement patterns namely (i) extension, (ii) flexion, (iii) eversion and (iv) inversion. Experimental results indicated the ability to identify thermal blobs of participants performing the 4 fundamental movement patterns of the human ankle. Cluster-based analysis of data gleaned from the ITA-32 sensors and the FMCW Radar sensors indicated average classification accuracy of 96.9% with K-Nearest Neighbours, Neural Network, AdaBoost, Decision Tree, Stochastic Gradient Descent and Support Vector Machine, amongst others.

The digital world has a wealth of data, such as Internet of Things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on "Data Science'' including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

Mapping forest extent and forest cover classification are important for the assessment of forest resources in socio-economic as well as ecological terms. Novel developments in the availability of remotely sensed data, computational resources, and advances in areas of statistical learning have enabled fusion of multi-sensor data, often yielding superior classification results. Most former studies of nemoral forests fusing multi-sensor and multi-temporal data have been limited in spatial extent and typically to a simple classification of landscapes into major land cover classes. We hypothesize that multi-temporal, multi-censor data will have a specific strength in further classification of nemoral forest landscapes owing to the distinct seasonal patterns of the phenology of broadleaves. This study aimed to classify the Danish landscape into forest/non-forest and further into forest types (broadleaved/coniferous) and species groups, using a cloud-based approach based on multi-temporal Sentinel 1 and 2 data and machine learning (random forest) trained with National Forest Inventory (NFI) data. Mapping of non-forest and forest resulted in producer accuracies of 99% and 90 %, respectively. The mapping of forest types (broadleaf and conifer) within the forested area resulted in producer accuracies of 95% for conifer and 96% for broadleaf forest. Tree species groups were classified with producer accuracies ranging 34-74%. Species groups with coniferous species were the least confused whereas the broadleaf groups, especially Oak, had higher error rates. The results are applied in Danish National accounting of greenhouse gas emissions from forests, resource assessment and assessment of forest biodiversity potentials.

Obesity is a global public health problem and the environment as its major determinant. To identify interventions an evidence base is warranted. To this aim we investigate the relationship between the consumption of foods and eating locations (like home, school/work and others) in British adolescents, using data from the UK National Diet and Nutrition Survey Rolling Program (2008–2012 and 2013-2016). Cross-sectional analysis of 62,523 food diary entries from this nationally representative sample then focused on foods contributing up to 80% total energy to the daily adolescent´s diet. Correspondence Analysis (CA) was first used to generate food-location relationship hypotheses and Logistic Regression (LR) to quantify the evidence in terms of odds ratios and formally test those hypotheses. The less-healthy foods that emerged from CA were chips, soft drinks, chocolate and meat pies. Adjusted Odds Ratios (99% CI) for consuming specific foods at a location “Other” than home (H) or school/work (S) in the 2008-12 survey sample were: for soft drinks 2.8 (2.1 to 3.8) vs. H and 2.0 (1.4 to 2.8) vs. S; for chips 2.8 (2.2 to 3.7) vs. H and 3.4 (2.1 to 5.5) vs. S; for chocolates 2.6 (1.9 to 3.5) vs. H and 1.9 (1.2 to 2.9) vs. S; and for meat pies 2.7 (1.5 to 5.1) vs. H and 1.3 (0.5 to 3.1) vs. S. These trends were confirmed in the 2013-16 survey sample. Interactions between location and BMI were not significant in either sample. In conclusion, our study showed that adolescents are more likely to consume specific less-healthy foods at locations away from home and school/work, irrespective of BMI. Such locations include leisure places, food outlets and “on the go”, hence public health policies to discourage less-healthy food choices in these locations is warranted for all adolescents.

A Natural gas is increasingly being sought after as a vital source of energy, given that its production is very cheap and does not cause the same environmental harms that other resources, such as coal combustion, do. Understanding and characterizing the behavior of natural gas is essential in hydrocarbon reservoir engineering, natural gas transport, and process. Natural gas compressibility factor, as a critical parameter, defines the compression and expansion characteristics of natural gas under different conditions. In this study, a simple second-order polynomial model based on the group method of data handling (GMDH) is presented to determine the compressibility factor of different natural gases at different conditions, using corresponding state principles. The accuracy of the model evaluated through graphical and statistical analyses. The results show that the model is capable of predicting natural gas compressibility with an average absolute error of only 2.88%, a root means square of 0.03, and a regression coefficient of 0.92. The performance of the developed model compared to widely known, previously published equations of state (EOSs) and correlations, and the precision of the results demonstrates its superiority over all other correlations and EOSs.

There is much discussion regarding the Sustainable Development Goals’ (SDGs) capacity to promote inclusive development. While some argue that they represent an opportunity for goal-led alignment of stakeholders and evidence-based decision-making, other voices express concerns as they perceive them as a techno-managerial framework that measures development according to quantitatively defined parameters and does not allow for local variation. We argue that the extent to which the positive or negative aspects of the SDGs prevail depends on the monitoring system’s ability to account for multiple and intersecting inequalities. The need for sub-nationally (urban) representative indicators poses an additional methodological challenge – especially in cities with intra-urban inequalities related to socio-spatial variations across neighbourhoods. This paper investigates the extent to which the SDG indicators’ representativeness could be affected by inequalities. It does so by proposing a conceptual framing for understanding the relation between inequalities and SDG monitoring, which is then applied to analyse the current methodological proposals for the indicator framework of the “urban SDG”, Goal 11. The outcome is a call for 1) a more explicit attention to intra-urban inequalities, 2) the development of a methodological approach to “recalibrate” the city-level indicators to account for the degree of intra-urban inequalities, and 3) an alignment between methodologies and data practices applied for monitoring SDG 11 and the extent of the underlying inequalities within the city. This would enable an informed decision regarding the trade-off in indicator representativeness between conventional data sources, such as censuses and household surveys, and emerging methods, such as participatory geospatial methods and citizen-generated data practices.

Affective computing in general and human activity and intention analysis in particular, is a rapidly growing field of research. Head pose and emotion changes, present serious challenges when applied to player’s training and ludology experience in serious games or analysis of customer satisfaction regarding broadcast and web services or monitoring a driver’s attention. Given the increasing prominence and utility of depth sensors, it is now feasible to perform large-scale collection of three-dimensional (3D) data for subsequent analysis. Discriminative random regression forests was selected in order to rapidly and accurately estimate head pose changes in unconstrained environment. In order to complete the secondary process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise), emotion recognition via facial expressions (ERFE) was adopted. After that, a lightweight data exchange format (JavaScript Object Notation-JSON) is employed, in order to manipulate the data extracted from the two aforementioned settings. Motivated by the need of generating comprehensible visual representations from different sets of data, in this paper we introduce a system capable of monitoring human activity through head pose and emotion changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor).

Maintaining a healthy cyber society is a big challenge due to the users’ freedom of expression and behaving. It can be solved by monitoring and analyzing the users’ behavior and taking proper actions towards them. This research aims to present a platform that monitors the public content on Twitter by extracting tweet data. After maintaining the data, the users’ interactions are analyzed using Graph Analysis methods. Then the users’ behavioral patterns are analyzed by applying Metadata Analysis, in which the timeline of each profile is obtained; also, the time-series behavioral features of users are investigated. Then in the Abnormal Behavior Detection Filtering component, the interesting profiles are selected for further examinations. Finally, in the Contextual Analysis component, the contents will be analyzed using natural language processing techniques; A binary text classification model (SVM + TF-IDF with 88.89% accuracy) for detecting if the tweet is related to crime or not. Then, a sentiment analysis method is applied to the crime-related tweets to perform aspect-based sentiment analysis (DistilBERT + FFNN with 80% accuracy); because sharing positive opinions about a crime-related topic can threaten society. This platform aims to provide the end-user (Police) suggestions to control hate speech or terrorist propaganda.

In the field of recommender systems, difficult negative samples that are easily misclassified by classifiers have higher learning value. In order to solve the problem that the existing graph convolutional network lacks the exploration of negative sampling strategy, a multivariate sampling based on graph convolution collaborative filtering recommendation model was proposed. Firstly, part of the information in the positive sample was injected into the negative sample, and the high quality difficult negative sample was selected by the inner product method. Then, contrastive learning is used to maximize the similarity between the difficult negative samples and the positive samples, so that the difficult negative samples are as close to the positive samples as possible in the feature space. Ultimately, a multi-task approach was employed to concurrently enhance both the recommendation supervision task and the contrastive learning task. To validate the effectiveness of our approach, we conducted comparative evaluations utilizing three publicly accessible datasets. The experimental outcomes indicate that our proposed model surpasses the baseline model in terms of performance.

Objective: To explore the acceptability and feasibility of conducting comprehensive, community-based, hy-brid (in-person and virtual) mental health screening and feedback with diverse youth in the community. Methods: The Screening and Support for Youth intervention provides comprehensive assessment and thera-peutic feedback in community settings including symptoms, diagnoses, functional impairment, suicide, and clinical risk. Symptom severity, seven diagnoses and suicidality were assessed with the Kiddie Computer-ized Adaptive Test. Functional impairment was evaluated by youth self-report on the Weiss Functional Im-pairment Rating Scale. Measures were scored according to nationally developed norms and an algorithm was developed to aggregate symptoms and functioning to determine clinical risk. The results were discussed with participants in a motivational interview to promote insight and engagement in an online intervention. Results: Methodological innovations included using a QR code for recruitment, integrated in-person and virtual participation, and expansion from in-person recruitment within the schools to virtual engagement with youth throughout the community. The final sample included disproportionately more Black or African American and Hispanic youth as compared to school and community statistics, suggesting that hybrid re-search may facilitate recruitment of diverse populations. Qualitative interviews indicated that the screening and feedback raised youth awareness of their wellbeing and/or distress, its impact on their functioning, and engagement with options for improving wellbeing. Conclusion: Hybrid research, developed as a necessity during the pandemic, proved advantageous to the feasibility and acceptability of community-based recruit-ment of at risk, minoritized youth.

The learning system is primarily based on teaching, and teaching through digital resources is referred to as e-learning. As the world progresses towards success day by day, the use of technology, digital devices, and modern techniques is becoming increasingly prevalent. The global shift from traditional paperwork to digital learning is one of the most significant changes in the education sector. However, many factors have created numerous hurdles during the online setup. The Covid-19 pandemic has led to serious problems all over the world, with the education sector being one of the most affected. One of the greatest challenges students face during online education is the lack of digital infrastructure, especially in remote areas of Pakistan. The biggest challenge is connecting students with teachers due to limited internet access. Without proper internet connectivity, both students and teachers cannot access educational materials. In this context, the government plays a crucial role in providing adequate facilities to students, particularly in primary schools. If proper infrastructure is not made available during this pandemic, students will be deprived of a good quality education. Therefore, good infrastructure is essential for effective online education. With the proper resources in place, students will be able to receive a quality education.

Objective: To explore the effect of time exposure to flat screen electronic devices with LED 14 lighting and the Mediterranean diet on macular pigment optical density (MPOD). Methods: MPOD 15 was measured by heterochromatic flicker photometry in 164 eyes (47 of young women 20-31 years 16 and 35 adult women 42-70 years). Exclusion criteria: evidence of macular degeneration and eyes 17 with cataracts. Data on the use of electronic devices and Mediterranean diet adherence were col- 18 lected through a survey. Nonparametric analysis of variance and independent sample t-tests were 19 used to compare subjects. Results: Significant differences (p<0.01) were found in total time of expo- 20 sure to LEDs (hours per day) between both groups (9.31 ± 3.74 young women vs 6.33 ± 3.64 old 21 women). The MPOD values for the young and adult populations were significantly different: 0.38 ± 22 0.16 and 0.47 ± 0.15 (p<0.01), respectively. When comparing both groups for the same time of expo- 23 sure to LEDs, differences were obtained between MPOD values of both populations: For total expo- 24 sures greater than 6 hours per day, the MPOD values were lower in young women than in adult 25 ones (0.37 ± 0.14 vs 0.50 ± 0.14, p<0.01). On the other hand, a significantly higher adherence was 26 found in the old women in comparison with the young women (OW 9.23± 2.50 vs YW 7.70 ± 2.08, 27 p<0.01), with higher MPOD values, (OW (0.52 ± 0.14) vs (YW (0.34 ± 0.18). Conclusions: Higher 28 MPOD values are observed with decreasing exposure time to electronic devices with LED lighting 29 screens and high adherence to the Mediterranean diet.

Background: Mitral regurgitation (MR) is a common valvular disorder linked to high morbidity and mortality. For patients unsuitable for surgery, transcatheter mitral edge-to-edge repair (TEER) with the MitraClip® G4 system offers an alternative. This study aims to evaluate procedural, echocardiographic, functional, and quality of life (QoL) outcomes in patients who underwent TEER with the MitraClip® G4 system, along with possible predictors of New York Heart Associa-tion (NYHA) class I at 30 days and 1 year. Methods: Patients with moderate-to-severe (3+) or se-vere (4+) degenerative MR (DMR) or functional MR (FMR), classified as NYHA class III or IV, who underwent TEER with the MitraClip® G4 system at our center between January 2021 and De-cember 2023 were included. Results: A total of 83 patients [71% FMR, 66% male, median (IQR) age 70 (11) years] underwent TEER, with 100% procedural success. MR ≤2+ was achieved in 100% and 98% of patients at 30 days and 1 year, respectively. NYHA class I or II was achieved in 100% and 96.8% of patients at 30 days and 1 year, respectively. The Kansas City Cardiomyopathy Ques-tionnaire (KCCQ) score improved from 51±20 at baseline to 69±15 at 30 days (p< 0.001) and 70.5±15 at 1 year (p< 0.001). Lower baseline N-terminal pro-brain natriuretic peptide (NT-proBNP) pre-dicted achieving NYHA class I at 30 days (HR: 0.63, 95% CI: 0.41–0.95, p = 0.030), while lower Eu-ropean System for Cardiac Operative Risk Evaluation II (EuroSCORE II) and NT-proBNP pre-dicted it at 1 year [(HR: 0.50, 95% CI: 0.28–0.89, p = 0.019), (HR: 0.67, 95% CI: 0.44–0.99, p = 0.049), respectively]. Conclusions: The MitraClip® G4 system provides significant improvements in MR severity, functional class, and QoL. Lower NT-proBNP and EuroSCORE II were strong predictors of achieving optimal functional status (NYHA class I).

This study examines the feasibility and efficacy of utilizing generative AI technology to create inclusive online dating educational materials, focusing on integrating easy-to-read principles to enhance accessibility while improving efficiency and reducing the cost of material production. As online dating becomes increasingly prevalent, providing easily comprehensible safety education for special populations, such as individuals with disabilities and adolescents, is of paramount importance. This research developed an innovative AI-generated educational framework that combines easy-to-read principles with generative AI technology, aiming to enhance both the accessibility and production efficiency of educational materials. The study compared three AI generation strategies: independent generation by ChatGPT, independent generation by Claude, and Claude utilizing materials generated by ChatGPT. We evaluated 36 educational units through a mixed-methods approach for readability and accessibility. Results indicate that ChatGPT performed optimally in generating easily readable content (average compliance rate of 93.00%), followed by the strategy Claude using ChatGPT-generated materials (91.67%). Statistical analysis revealed significant differences among these strategies (p < 0.05). The research also found that employing AI generation technology significantly reduced material production time and costs, providing educators with a more efficient tool. The findings of this study underscore the significant potential of generative AI technology in creating inclusive educational resources. This not only opens up new avenues for enhancing online safety awareness and educational accessibility for special populations but also provides material developers with more efficient, cost-effective production methods. The empirical evidence presented here for the application of AI in inclusive education is a significant step forward. The study also proposes recommendations for future research and practice, aiming to further improve the accessibility, effectiveness, and production efficiency of online dating education.

Background: Inflammatory Bowel disease (IBD) is one of the worldwide health issues with increasing incidence. Recent data point out that those patients can have a defective immune system, rendering response to vaccinations suboptimal. Since 1990, Hepatitis B Virus (HBV) vaccination has been introduced among the compulsory childhood vaccinations in Oman. Given the questionable immunogenicity of vaccinations in IBD patients, we designed this study to quantify the immune response to scheduled HBV vaccine in IBD patients and compare it with that in a matching control cohort of healthy blood donors at Sultan Qaboos University Hospital (SQUH). Methods: This is a retrospective case-control study that was conducted between the 1st of July 2023 and the 31st of May 2024 at SQUH. Using the hospital information System (HIS), a total of 252 participants were enrolled in our study and divided into two groups. Group (1) included 126 IBD patients and group (2) included 126 healthy controls. Baseline demographics, medical comorbidities and viral serologies before and after vaccination were collected. The primary outcome of the study was to compare the long term immune response to HBV vaccine between the two groups. The secondary outcomes included identifying possible risk factors for suboptimal vaccine response. Results: One hundred and twenty-six participants were included in each group. Durable protective immune response to HBV vaccine was attained in 29 patients (23%) of the IBD group compared to 109 participants (86.5%) of the control group. There was a statistically significant association (p = 0.0156) between prednisolone treatment and low immune response to HBV vaccine. Moreover, esomeprazole usage exhibited a statistically significant association (p < 0.0001) with suboptimal response to HBV vaccine. Meanwhile, there was no statistically significant correlation between low immunogenicity to HBV vaccine and age, sex, type of IBD, comorbidities, and BMI. Conclusions: IBD patients are less immunogenic to HBV vaccination compared to healthy controls. Factors like esomeprazole and/or corticosteroid therapy might be associated with poor immune response. These findings highlight the importance of reassessing the immunogenicity of HBV vaccination and probably other protective vaccinations in all IBD patients as part of their standard of care.

Emotional agility and social intelligence at small to medium-sized enterprises (SMEs) can be an invigorating experience, filled with opportunities for growth, innovation, and collaboration. This research aims to investigate the Omani entrepreneurs’ emotions and intelligence at workplace through self-experiences in SMEs. A sample of 222 Omani entrepreneurs participated in online survey that was designed to attain the objectives of this research. Data was collected using google drive and analyzed using multiple regression path analysis. The results revealed that emotional agility and social intelligence have positive and significant impact on employee thriving at work in Omani SMEs. Further, openness to experience mediates the relationships between entrepreneurs’ emotions and intelligence and sustainable workplace. These results confirm the effective role of openness to experience among Omani entrepreneurs where they should adopt new strategies and techniques to improve their skills and support their SMEs. This research highlighted the role of interpersonal skills including agility and intelligence of entrepreneurs in Arab context.

The elasticity of red blood cells (RBCs) is crucial for their ability to fulfill their role in the blood. Decreased RBC deformability is associated with various pathological conditions. This study explores the application of machine learning to predict the elasticity of RBCs using both image data and detailed physical measurements derived from simulations. We simulated RBC behavior in a microfluidic channel. The simulation results provided the basis for generating data on which we applied machine learning techniques. We analyzed the surface-area-to-volume ratio of RBCs as an indicator of elasticity, employing statistical methods to differentiate between healthy and diseased RBCs. The Kolmogorov–Smirnov test confirmed significant differences between healthy and diseased RBCs, though distinctions among different types of diseased RBCs were less clear. We used decision tree models, including random forests and gradient boosting, to classify RBC elasticity based on predictors derived from simulation data. The comparison of the results with our previous work on deep neural networks shows improved classification accuracy in some scenarios. The study highlights the potential of machine learning to automate and enhance the analysis of RBC elasticity, with implications for clinical diagnostics.

According to current forecasts, global heating is likely to exceed 2,8°C by the end of the century. This makes substantial adaptation measures necessary to secure a broad basis for livelihoods provision and the conservation of biodiversity. While the implementation of top-down and technocratic adaptation efforts is the majority, related adaptation shortcomings of socio-economic and ecological nature are becoming more and more apparent. Community-based adaptation (CBA) with its participatory, inclusive and needs-based bottom-up approach offers a promising and powerful alternative. This article uses a semi-systematic literature review approach to screen the current literature and identify core issues of CBA. Linking communality, locality, multidimensionality, power imbalances, transformative potential, localisation, the triad of adaptation metrics and adaptation with nature to corresponding potential actions for practical implementations provides a more holistic conceptualisation and broadens the horizons for further learnings, research and improved application.

Here, we report an ultrasoft extra long-lasting reusable hydrogel-based sensor that enables high quality electrophysiological recording with low motion artifacts. The developed sensor can be used and stored in the ambient environment for months before being reused again. The developed sensor is made of a self-adhesive electrical-conductivity-enhanced ultrasoft hydrogel, mounted in an Ecoflex-based frame. Hydrogel’s conductivity was enhanced by incorporating polypyrrole (PPy), resulting in the conductivity of 0.25 S m-1. Young’s modulus of the sensor is only 12.9 kPa and it is stretchable up to 190%. The sensor was successfully used for the electrocardiography (ECG) and electromyography (EMG). Our results indicate that using developed hydrogel-based sensor, the signal-to-noise ratio of recorded electrophysiological signals was improved in comparison to that when medical grade silver/silver chloride (Ag/AgCl) wet gel electrodes were used (33.55 dB in comparison to 22.16 dB). Due to the ultra-softness, high stretchability, and self-adhesion of the developed sensor, it can conform to the skin and therefore, it shows low susceptibility to motion artifacts. Also, the sensor shows no sign of irritation or allergic reaction that usually occurs after long-term wearing medical grade Ag/AgCl wet gel electrodes on the skin. Further, the sensor is fabricated using a low cost and scalable fabrication process.

This paper presents the five-parameter Exponentiated Weibull Weibull (EWW) distribution with Weibull base failure rate parameterization, developed to model the dropout times of students in an online class module. Parameters of the model were estimated using both Maximum Likelihood and Bayesian estimation procedures to determine the most effective estimation method. Simulation results indicate that, across various hazard types and censoring levels, Bayesian estimates are more accurate and precise than Maximum Likelihood estimates. Real-life data analysis of online students module presentation supports these findings, showing significantly lower standard deviations for Bayesian estimates compared to Maximum Likelihood estimates, highlighting the efficiency of Bayesian estimation for the EWW distribution. Additionally, a comparative goodness-of-fit analysis demonstrated that the EWW distribution better fits the student dropout times dataset than Weibull Weibull (WW), Weibull Exponential (WE), and Exponential Weibull (EW) distributions. The median dropout time estimated using the EWW distribution is 24 days, suggesting that instructors should closely monitor student attendance around this period.

Klebsiella pneumoniae, a member of the ESKAPE pathogen group, is a prominent cause of hospital-acquired infections. The WHO recognized carbapenem-resistant K. pneumoniae as a critical-one priority pathogen. These resilient superbugs have the ability to form biofilms and present a significant global threat. In the present study, we isolated and characterized a bacteriophage SAKp02, from hospital sewage, infectious to carbapenem-resistant K. pneumoniae patient isolates. SAKp02 could infect 43 of 72 clinical isolates, indicating a broad host spectrum. Whole genome analysis classified SAKp02 within the family Casjensviridae, with a 59,343 bp genome encoding 82 ORFs. Comparative genomic analysis revealed significant differences between SAKp02 and its closest viruses, indicating a distinct genetic makeup positioning it as a novel phage strain within the lineage. The SAKp02 genome comprises bacteriolytic enzymes, including holin, endolysin, and phage depolymerase, crucial for bacterial lysis and biofilm disruption. It reduced biofilm biomass by over threefold compared to the control and eradicated 99% of viable cells within a 4-hour treatment period. Scanning electron microscopy corroborated the ability of the phage to dismantle biofilm matrices and lyse bacterial cells. Safe and effective treatments are warranted, and hence, the fully characterized lytic phages with therapeutic potential against drug-resistant clinical isolates of bacteria are needed. Our study is the first to report the antibacterial and antibiofilm activity of Casjensviridae phages, and our discovery of a novel K. pneumoniae phage broadens the arsenal against the bacteria.

: Carcinoma-associated fibroblasts (CAFs) are highly accumulated in the tumor surrounding stroma of primary epithelial ovarian cancer (OC). CAFs exert important functions for the vascularization, growth and progression of ovarian tumor cells. However, no attention has been shown to their origin so far. It has been mainly assumed that CAFs in primary OC arise from the activation of resident fibroblasts. Here, we compare CAFs with ovarian location to CAFs found in peritoneal implants from patients with advanced OC. Our findings show that CAFs from primary tumors and peritoneal metastases share the expression of mesothelial-related markers. Therefore, as in peritoneal carcinomatosis, CAFs in primary ovarian carcinomas could be originated via a mesothelia-to-mesenchymal transition (MMT) process. The detection of mesothelial-derived CAFs in tumors confined to the ovary and associated biomarkers could be the key to early detection of peritoneal spread in affected patients.

This article addresses the emergence of a niche for sustainable agricultural production in the province of García Rovira, Colombia. It examines the intervention of the Colombian planning regime, which implements agendas for sustainable territorial development. However, these agendas are not adequately aligned with provincial realities, resulting in a productive decline. From a multi-level perspective, a niche of production linked to the landscape is identified, highlighting sustainable practices and innovation. The study seeks to understand how planning influences the emergence of such niches and what factors boost their transition to sustainability. Through a mixed methodology, growing sectors are highlighted, and the profiles of the producers involved are outlined. The main conclusion is that the planned intervention is inefficient, as the niche of sustainable production emerges without its clear influence. Therefore, it is proposed to re-evaluate planning using alternative analysis frameworks to detect emerging productive dynamics, serving as a basis for future plans.

This paper presents a novel framework for Collaborative Learning Networks (CLNs) in classrooms, aiming to optimize resource utilization and enhance educational services. The study addresses the inefficiencies of traditional education models, particularly the underutilization of computational resources and limited access to specialized software. The proposed framework employs a mix of task-based and random allocation strategies to distribute computational tasks within the CLN, considering each device’s capabilities and task requirements. The findings show significant improvement in resource utilization, facilitated by device-to-device collaboration and task sharing, thus enriching students’ learning experiences. This work contributes to optimizing resource utilization in education, advancing the field of CLNs.

Recent years have witnessed significant advances in reinforcement learning (RL), which has registered remarkable success in solving various sequential decision-making problems in machine learning. Most of the successful RL applications, e.g., the games of Go and Poker, robotics, and autonomous driving, involve the participation of more than one single agent, which naturally fall into the realm of multi-agent RL (MARL), a domain with a relatively long history, and has recently re-emerged due to advances in single-agent RL techniques. Although, typically, the communication protocol between agents is manually specified and not altered during training, recently, some papers have shown signs of trying to emerge a communication between agents on the one hand and, on the other hand, to understand what is exchanged between agents. So, there is a growing body of literature on this topic which includes qualitative and quantitative studies and the ones that apply mixed methods. This study presents the scoping review of the methodological strategies undertaken in a total of 16 research articles. The results present the critical appraisal of quantitative methods in terms of validity and reliability and for qualitative methods considering four trustworthiness factors. In the end, relevant insights are further explored with implications and reflections on how they can benefit one's research in the field.

Agriculture contributes towards South African economy, but this sector is highly vulnerable to risks imposed by climate change. Smallholder maize farmers are specifically susceptible to climate change impacts. Maize crop plays a crucial role in the country's food security as it is considered a stable food for households and source of feed for livestock. The study aimed to analyze the socio-economic factors influencing smallholder maize farmers’ willingness to adopt climate-smart agriculture. It was conducted in three different areas due to their specific agro-ecological zones. Multipurpose research design was used to gather data, and multistage random sampling to choose the study areas. Subsequently, 209 purposively selected farmers were interviewed face-to-face using structured questionnaires and focus discussion groups. Descriptive statistics and Double-hurdle regression model were used to analyze the data. Descriptive results indicated that 81%, 67%, and 63% farmers were willing to adopt CSA in Ga-Makanye, Gabaza, and Giyani, respectively. Empirical results from the first and second hurdles of the regression analysis showed that farmers’ education, crop diversification, information about CSA positively influenced the decision to adopt CSA while household size and agricultural experience negatively influenced the decision to adopt CSA. It is recommended that the Department of Agriculture, Land Reform, and Rural Development should provide CSA workshops and educational programs to farmers to enhance their knowledge and decision-making processes regarding adaptation strategies.

The global craft beer market is witnessing substantial growth, with a particular spotlight on sour beers. These unique brews are created through lactic fermentation initiated by lactic acid bacteria (LAB). However, the presence of hops, renowned for their antimicrobial properties, can pose a limitation within the realm of sour beer production. This research aims to explore and assess the potential of sour beer as a probiotic beverage. It involves a comparative analysis of the viability of Lacticaseibacillus paracasei subsp. paracasei F19 (F19) and 431 (L431) in a sour beer with an International Bitterness Unit (IBU) value of 26.65, alongside examining the ex-pression of genes such as horA, horB, horC, hitA, bsrA, and recA, which are associated with hop resistance. The study encompasses the development of two formulations of sour beers characterized by both high hop content and a robust probiotic count (8.44 – 8.77 log CFU/mL). The findings suggest that both probiotic strains of F19 and L431, are well-suited to produce sour beers with elevated hop levels, demonstrating excellent stability. Notably, the expression of genes responsible for hops resistance exhibited distinct modulation patterns among the two strains. It appears that a higher concentration of bsrA, as observed in strain L431, may be more effective in mitigating the effects of hop-related stress, potentially surpassing the combined expression of horA and bsrA. Nonetheless, further research is essential to validate this observation in gene expression. F19 and L431 emerge as promising candidates for the development of sour beers with increased hop intensity.

This study investigates the impact of brand lovemark on three customer behavioral outcomes (i.e., green brand loyalty, willingness to pay a deposit, and machine use intention) in coffee shops’ CSR practices. We used a scenario-based experimental design on CSR practices in two coffee shop brands (i.e., Starbucks and Dunkin'). The study demonstrates that consumers with a high level of brand lovemark respond more positively to CSR practices of coffee shops than do customers with a low level of brand lovemark. In addition, the interactions between brand lovemark and gender appear to be important in all three behavioral outcomes. Specifically, it confirms that men and women show different levels of behavior outcomes depending on their level of brand lovemark. Theoretical and managerial implications are also suggested with the direction for future study.

Gas turbine engines are widely used in various industries such as aviation, power generation, and marine propulsion due to their high efficiency and reliability. The combustion chamber of a gas turbine engine design contains a number of elements, including as the chamber's size and form, the fuel injection system, the ignition system, and the cooling system and it plays a critical role in the performance and efficiency of the engine. This research discusses the fabrication of a combustion chamber model for a gas turbine engine. The aim of this research is to develop a combustion chamber that is cost-effective, easily available, and can be fabricated in less time. The research covers the different types of combustion chambers used in gas turbine engines and compares their advantages and disadvantages. It then selects the best type of combustion chamber for the gas turbine engine based on its cost, availability, and fabrication time, Efficiency and high power to fuel ratio. The report also describes the fabrication process involved, including the techniques and materials used, along with safety precautions and environmental protection measures. Lastly, the research suggests future improvements and potential applications for the fabricated combustion chamber model.

As both governments and customers push for sustainable and recyclable textile products, textile companies will have to change their linear business model to a circular one. The aim of the present work is to help these companies take action by providing an update of the state-of-the-art of textile recycling technologies. Extensive desktop research was performed in order to prepare an overview of existing textile recycling technologies, their current state and prognosis, including also facilitating technologies for sorting and disintegration of textile products as well as virtual platforms that connect stakeholders and inform consumers.

Wellness tourism is a growing segment that destinations can rely on to increase tourism flows and revive underdeveloped rural areas. This study elicits tourists' preferences for wellness tourism products and the redevelopment of a wellness destination in a depopulated rural area. The re-search included 595 participants in Central Greece using on-site, face-to-face surveys. The survey data was analyzed using a two-step process. First, conjoint analysis was applied to determine the combination of wellness tourism product attributes that maximize visitors’ utility. In the second step, we elicited visitors' willingness to pay (WTP) for improvements in tourism product attributes using the contingent valuation method. Findings indicate that the combination of attributes that maximizes the preference structure of respondents includes time of visit from September to Oc-tober, duration of visit from 1 to 7 days, wellness tourism as the primary purpose of the visit, and wellness activities that contain both physical activities and training as well as spiritual and emo-tional enhancement. Using binary logistic regression, the analysis indicates that respondents are willing to pay 47.33€ per visit on average further to develop wellness tourism products in the study area. Empirical results confirmed that sustainability issues, quality, and entrepreneurship factors positively impact visitors' WTP for revitalizing wellness products. In contrast, a longer duration of stay in the area and respondents' age negatively impact their WTP. These findings suggest that structuring a customer-driven tourism product will enhance the rural destination's efforts to attract high-yield visitors and advance the rural economy. Furthermore, practical im-plications reveal that Destination Management Organizations will benefit from research results to foster targeted management plans.

This study proposes self-calibration of capacitor mismatch errors for high-resolution pipeline analog-to-digital converters (ADCs). The proposed calibration circuit recursively amplifies the capacitor mismatch error by re-utilizing a multiplying digital-to-analog converter in a pipeline stage without increasing the circuit complexity, and the amplified error voltage is converted into digital code by utilizing the remaining pipeline stages. Error correction is performed by subtracting the digital code from the ADC output during normal operation. A prototype of a 12-bit pipeline ADC is fabricated in a 0.18 µm standard CMOS process. The ADC comprises eight 1.5-bit stages, followed by a 4-bit flash ADC as the final stage; the capacitor mismatch errors in the first two pipeline stages are corrected by utilizing the proposed self-calibration technique. At a sampling rate of 30 MS/s, the measured differential and integral nonlinearities improve from +0.82/-0.75 and +1.12/-1.79 to +0.45/-0.41 and +0.47/-0.91 after calibration, respectively. Further, the measured dynamic performances improve significantly with calibration. For an input frequency of 2.09 MHz, the ADC achieves a spurious-free dynamic range (SFDR) and signal-to-noise and distortion ratios (SNDR) of 69.3dB and 63.9dB before calibration, respectively. After calibration, the SFDR and SNDR increase to 84.1dB and 68.9dB, respectively, and the effective number of bits is approximately 11.1bit. The power consumption of the ADC is 35mW under 1.8V supply, and the resulting figure of merit is 514fJ/conversion step.

Current methods for the hardening step of functional coatings over different materials imply the use of high temperatures, high energy consumption or long periods of time, which have repercussions on the speed and cost of the product. We report here a simple and low cost methodology for the functionalization of low cost stainless steel, which is modular, depending of the functionality pursued: a levelling layer for smooth the surface of the material, an “easy to clean” property, or both of them. The research is based on sol-gel coatings cured under UV light without requiring high thermal hardening processes making it applicable to plastics and other sensible materials and possessing high chemical and thermal stability. The film ensures lower processing costs, and higher rates of hardening if the adequate medium-pressure lamps are employed. This formulation is also well defined for scaling up the process, so it is possible to perform a continuous coating in large areas by employing mild processing conditions (low temperature, atmospheric pressure). In addition, the sol gel solution was fully characterized and studied in order to guarantee a long service life before deposition, with a focus on industrial applications in the domestic sector.

High temperature titanium alloys are widely used in aerospace hot parts, however, the thermal barrier temperature of 600 oC limits its service temperature. In this paper, a two-step hot-dip plating method is proposed to prepare the composite coating containing Ce on the surface of titanium alloy, which can make the oxidation resistance temperature of titanium alloy reach 800 oC The microstructure, phase composition and element distribution of Ce-containing Ti-Al-Si hot-dip coating, Ce-containing Ti-Al-Si pre-oxidation coating and Ce-containing Ti-Al-Si high-temperature oxidation coating were studied by X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS), respectively. The results show that the Ti-Al-Si hot-dip coating containing Ce is mainly composed of (Ti,Ce)(Al,Si)3 alloy phase layer, and the Ti-Al-Si pre-oxidation coating containing Ce is mainly composed of Ti-Al binary system phase layer and dense Al2O3 layer rich in CeO2. Ce in the high temperature oxidation coating of Ti-Al-Si containing Ce is mainly distributed in Ti3Al, TiAl, TiAl3+Ti5Si3 mixed phase layer and Al2O3. The addition of Ce can promote the formation of continuous Al2O3 layer, improve the uniformity of composite coating, prevent the spread of cracks, and improve the high temperature oxidation resistance of Ti-Al-Si coating.

Type 2 diabetes mellitus may be complicated with chronic complications including diabetic neuropathy (DN). Timely diagnosis is crucial in treatment and maybe reversal of this complication. C reactive protein to albumin ratio (CAR) is considered as a marker of inflammation in various diseases that characterized with acute or chronic inflammation. Since diabetic neuropathy and type 2 diabetes mellitus are also associated with chronic, low grade inflammation, we aimed to study CAR levels of type 2 diabetic subjects with diabetic neuropathy and to compare to those in patients without diabetic neuropathy. Type 2 diabetic patients presented to our institutional outpatient clinics were divided into two according to the presence of DN. Subjects with DN were listed as DN group and others were as non-DN group. Characteristics and laboratory data, including CAR, in DN and non-DN groups were compared. Median CAR of the DN and non-DN groups were 2,19 (0,2-49)% and 0,56 (0,02-5,8)%, respectively (p&lt;0.001). CAR was significantly and positively correlated with age (r=0.15, p&lt;0.001), weight (r=0.19, p=0.01), BMI (r=0.11, p=0.03), waist circumference (r=0.10, p=0.046), fasting plasma glucose (r=0.14, p=0.004), serum creatinine (r=0.25, p&lt;0.001), triglyceride (r=0.17, p&lt;0.001), and LDL-cholesterol (r=0.13, p=0.001) levels. Moreover, CAR was inversely correlated with eGFR levels (r=-0.16, p&lt;0.001). The sensitivity and specificity of CAR (when higher than 1,02%) in predicting diabetic neuropathy were 78% and 73%, respectively (AUC: 0.84, p&lt;0.001, 95% CI: 0,82-0,87). Moreover, high CAR level was an independent risk factor of diabetic neuropathy (p&lt;0.001, OR: 1.34, 95% CI: 0.48-1.62). In conclusion, elevated CAR levels could be considered as a marker of diabetic neuropathy.

Brewers’ spent grain (BSG) is an important waste produced in beer companies with a high po-tential to be transformed into commercial by-products. The present paper reports a forecasting technology study with the aid of bibliographic review and patent analysis tools aiming to in-vestigate the maturity of certain technologies, considering the use of BSG on a closed-loop bio-refinery and circular bioeconomy concept integrated into a brewery. To evaluate the possibili-ties of production of high-added value products in Brazil, it was presented an overview of BSG’s scientific, technological, and marketing products and applications. The comparison between numbers of articles versus patents shows that the solutions proposed by research articles are not being transformed into maturated viable technologies. The results suggest that there is a gap between the scientific research in the institutions and their applications in industry, which lead to the destination of the BSG for more economically attractive investments when compared to research in Brazil. Ultimately, from the carried-out analysis, it was possible to propose a brew-ing process connected to the biorefinery system, showing the possibility to the newly arising brewery industries.

Bacterial transformation is the essential tool for introducing foreign DNA into bacterial cells in a host of recombinant technology applications useful for biomanufacturing and drug production. Thus far, chemically competent cells remain an important enabling technology for bacterial transformation given its facile transformation approach. However, many parameters affect transformation efficiency, and there is relatively little documentation of their cross-interacting effect in the literature. This work explores the roles played by incubation time on ice, competent cell concentration, and plasmid-to-cell ratio on transformation efficiency in Escherichia coli DH5α. Results revealed that increases in incubation time improve transformation efficiency, but 2 minutes incubation still yield appreciable efficiency. On the other hand, low concentration competent cells (10⁶ CFU/mL) yielded significant improvement in transformation efficiency compared to high concentration competent cells (10⁸ CFU/mL). In terms of plasmid-to-cell ratio, higher ratio increases transformation efficiency. Overall, bacterial transformation is incompletely understood particularly in the area of cross-interacting parameters. Experiments reported here revealed the possibility of short duration transformation where lower competent cell concentration and higher plasmid-to-cell ratio could improve transformation.

Antibiotics are the most magnificent discovery of 20th century that have saved millions of lives from infectious diseases. Microbes have developed acquired antimicrobial resistance (AMR) to many drugs due to high selection pressure from increasing use and misuse of antibiotics over the years. The transmission and acquisition of AMR occur primarily via human–human interface both within and outside of the healthcare facilities. A huge number of interdependent factors related to healthcare and agriculture govern the development of AMR through various drug resistance mechanisms. The emergence and spread of AMR from the unrestricted use of antimicrobials in livestock feed has been a major contributing factor. The prevalence of AMR bacteria has attained its incongruous level worldwide and threatening global public health as silent pandemic, necessitating urgent intervention. Therapeutic options of AMR bacterial infections are limited resulting in significant morbidity and mortality with high financial impact. The paucity in discovery and supply of new novel antimicrobials to treat life-threatening AMR infections stands in sharp contrast to demand. Immediate interventions to contain AMR include surveillance and monitoring, minimizing over the counter antibiotics and antibiotics in food animals, access to quality and affordable medicines, vaccines and diagnostics, and enforcement of legislation. An orchestral collaborative action within and between multiple national and international organizations are required urgently, otherwise, a post-antibiotic era can be a real possibility than an apocalyptic fantasy for the 21st century. This narrative review highlights on the basis, mechanisms and factors in microbial resistance and key strategies to combat antimicrobial resistance.

Food demand and food supply have been heavily affected by Covid-19 pandemic. To understand changes in households’ behavior related to the pandemic, we investigate the willingness to pay for local organic apples pre- and during Covid-19. Besides, we assess whether the changes occurred within the family, estimating also separate models for the two members of the couple. Our findings show that respondents show a positive price premium for local organic apples whose consumption helps reducing the environmental costs associated with food production. The median estimated values during the pandemic range from 34% to 250%. Overall, respondents show a positive mean willingness to pay which increase with the pandemic. The socio-economic variables are the most important in explaining the willingness to pay, while the behavioral variables have more heterogeneous results even if the lifestyle and the request for information through the label are important variables. The pandemic tends to narrow the gaps in preferences between members of the couple. In conclusion, the local and organic dimension is embodied in the short chain in which knowledge and trust in the producer are crucial elements in the consumer's choices.

Drought stress is a major production constraint in crops globally. Crop wild relatives are important source of resistance and tolerance for both biotic and abiotic stresses, respectively. A breeding program was initiated to introgress drought tolerance in sunflower through hybridization between the wild species Helianthus argophyllus and the cultigen pool of H. annuus. Selection was carried out in the F2 to F5 segregating populations for high cuticular waxes, smaller leaf area, single heading and high oil content. The selected F5 breeding lines were compared with non-adapted elite sunflower germplasm in a replicated randomized complete block research project using different water treatments comparing fully irrigated (T0), with 75 (T1), 50 (T2) and 25% (T3) irrigation treatments. The comparison between the two types of germplasm showed that drought tolerant breeding lines had comparatively lesser decreases in leaf area, shoot weight and root shoot ratio 10 under the various drought treatments. Leaf area of the drought selected F5 lines were smaller than that of the elite germplasm under controlled irrigated treatments, but they maintained their leaf area under the drought treatments. Several drought resistant promising lines D-2, D-5, and D-27 were identified and showed high leaf area, great root length and increase root to shoot ratio under the highest stressed treatments (T3). Some of the lines could be directly used for the development of drought tolerant hybrids. Combining ability test indicated D-27 (F7) as good general combiner for seed yield plant−1 and oil contents when mated with male line RSIN.82. These hybrids could help to minimize seed yield losses due to water stress and to achieve profitable cultivation of sunflower in 18 arid regions of Pakistan.

In the context of Covid-19 virus containment, there is a lack of acceptance of preventive measures in the population. The present work investigated which factors influence the belief in of scientific propositions compared belief in conspiracy theories. The focus here was on the determinants of conspiracy beliefs in the context of Covid-19 related media content. Using an online questionnaire (N = 175), results indicate that scientific compared to conspiracy theoretical media content led to higher acceptance. Furthermore, Need for Cognition (NFC-K), a conspiracy theoretical worldview (CMQ), and openness to experience (NEO-FFI) were positively associated with conspiracy beliefs derived from Facebook postings. In addition, a conspiracy theoretical worldview was negatively associated with belief in scientific media content. Furthermore, agreeableness was unrelated to conspiracy beliefs, although it was positively associated with conspiracy theoretical worldview. The results imply promising persuasion strategies for reducing conspiracy theoretical beliefs and to increase the acceptance of preventive measures.

Enteropathogens, namely Salmonella and Campylobacter, are a concern in global public health and have been attributed in numerous risk assessments to a poultry source. During the last decade a large body of research addressing this problem has been published. The literature reviewed contains review articles on certain aspects of poultry production chain, however in the past decade there hasn’t been a review on the through production chain, farm to fork, production of poultry. This review, a pool of 514 articles were selected for relevance via a systematic screening process (from >7500 original search articles). These studies identified a diversity of management and intervention strategies for the elimination or reduction of enteropathogens in poultry production. Many studies were laboratory or limited field trials with implementation in true commercial operations being problematic. Entities considering using commercial anti-enteropathogen products and interventions are advised to perform an internal validation and fit for purpose trial as Salmonella and Campylobacter serovars and biovars may have regional diversity. Future research should focus on non-chemical application within the processing plant and how synergistic through chain intervention may contribute to reducing the overall carcass burden of enteropathogen, coupled with increased consumer education on safe handling and cooking of poultry.

Extensive in-situ resource utilisation (ISRU) will be essential to enable long-duration stays on Luna and Mars and reduce reliance on resupply from Earth. Early development of ISRU technologies has focused on standalone capabilities for specific targets related to life support and ascent propellant. An unexplored opportunity remains for greatly expanding the scope of materials that can be supplied by ISRU, and for integrating various technology platforms into a larger system. Recent advances in power-to-X technology aimed at decarbonising the global economy have made it possible to drive key chemical processes using electricity with small-scale, modular reactor. This paper proposes a vision for an integrated system of ISRU processes based on power-to-X technology to produce oxygen, hydrogen, water, methane, polymers, metal alloys, and synthetic fertilisers, using Martian regolith, atmosphere, and ice. A ‘building block’ strategy is adopted to convert raw materials into versatile intermediaries, which can then be combined to form essential products. A wider range of raw materials are available on Mars compared to Luna, suggesting greater opportunity for ISRU deployment to compensate for the greater time and cost requirements for a Mars resupply mission.

Air corridors are an integral part of the advanced air mobility infrastructure. They are the virtual highways in the sky for transportation of people and cargo in the controlled airspace at an altitude of around 1000 ft. to 2000 ft. above the ground level. This paper presents fundamental insights into the design of air corridors with high operational efficiency as well as zero collisions. It begins with the definitions of air cube, skylane or track, intersection, vertiport, gate, and air corridor. Then, a multi-layered air corridor model is proposed. Traffic at intersections is analyzed in detail with examples of vehicles turning in different directions. The concept of capacity of an air corridor is introduced along with the nature of distribution of locations of vehicles in the air corridor and collision probability inside the corridor are discussed. Finally, the results of simulations of traffic flows are presented.

Current commitments by European governments seek to improve energy consumption as a means to reduce carbon emissions from building stock by 2050. Within such context, retrieving reliable three-dimensional contours from point clouds becomes an important step in developing facade retrofitting solutions since facade retrofitting projects often make use of as-built 3D models to help reduce inaccuracies by narrowing interpretation and measurement errors. This work aims to provide a method that uses topology-based parametric modelling for reconstructing building envelopes from point clouds. Through a semi-automated process that gives permanent visual feedback, the user adjusts parameters to custom standards of acceptability. A solution under the form of a Grasshopper definition delivers building envelope 3D contours in various file formats as a means for increasing interoperability. The main contributions of this work consist of a parametric reconstruction workflow capable of solving building topology for retrieving 3D contours, a strategy to bypass point cloud occlusion, and a strategy for converting those contours into an IFC model directly from the parametric modelling environment.

Autonomous systems require identifying the environment and it has a long way to go before putting it safely into practice. In autonomous driving systems, the detection of obstacles and traffic lights are of importance as well as lane tracking. In this study, an autonomous driving system is developed and tested in the experimental environment designed for this purpose. In this system, a model vehicle having a camera is used to trace the lanes and avoid obstacles to experimentally study autonomous driving behavior. Convolutional Neural Network models were trained for Lane tracking. For the vehicle to avoid obstacles, corner detection, optical flow, focus of expansion, time to collision, balance calculation, and decision mechanism were created, respectively.

The study is an attempt to enquire into the preference of undergraduate students, after a considerable and over a year-long experience with Online Teaching and Learning (OTL), under the emergency preventive measure of switching from the traditional face-to-face classes to online. The study followed an exploratory approach, with a quantitative survey followed by a qualitative one, and a convenient sampling method to collect responses from a substantial sample size. The study is positioned after more than a year of remote classes by the undergraduate student, and hence represents highly experienced reflections and preferences from these students, as compared to other studies conducted last year. The study has profound implications in considering, and questioning, the importance of on-campus classes, and significance of the physical presence of a tutor in the class, and its effect on the learning experience of undergraduate students.

The wide adoption of the recently coined fog and edge computing paradigms alongside conventional cloud computing creates a novel scenario, known as the cloud continuum, where services may benefit from the overall set of resources to optimize their execution. To operate successfully, such a cloud continuum scenario demands for novel management strategies, enabling a coordinated and efficient management of the entire set of resources, from the edge up to the cloud, designed in particular to address key edge characteristics, such as mobility, heterogeneity and volatility. The design of such a management framework poses many research challenges and has already promoted many initiatives worldwide at different levels. In this paper we present the results of one of these experiences driven by an EU H2020 project, focusing on the lessons learnt from a real deployment of the proposed management solution in three different industrial scenarios. We think that such a description may help understand the benefits brought in by a holistic cloud continuum management and also may help other initiatives in their design and development processes.

RNA editing involves the insertion, deletion or substitution of single nucleotides within a RNA molecule, without altering the DNA sequence. Adenosine to inosine (A-to-I) editing consists of an RNA modification where single adenosines along the RNA sequence are converted into inosines. Such a biochemical transformation is catalyzed by enzymes belonging to the family of adenosine deaminases acting on RNA (ADARs) and occurs either co- or post-transcriptionally. Initially, the A-to-I RNA editing phenomenon was discovered and studied in messenger RNAs (mRNAs), where it can influence RNA splicing and cause the recoding of codon sequences. The employment of more powerful, high-throughput detection methods has recently revealed that A-to-I editing widely occurs in non-coding RNAs, including microRNAs (miRNAs). MiRNAs are a class of small regulatory non-coding RNAs (ncRNAs) acting as translation inhibitors, known to exert relevant roles in controlling cell cycle, proliferation, and cancer development. Indeed, a growing number of recent researches have evidenced the importance of miRNA editing in cancer biology by exploiting various detection and validation methods. Herein, we briefly overview early and currently available A-to-I miRNA editing detection and validation methods and discuss the significance of A-to-I miRNA editing in human cancer.

Stakeholder input into the decision-making process when developing public health programs and policies is crucial. This article presents an innovative approach, involving online participation with a wide group of stakeholders located in different geographic locations, for policy consensus by research methodology. The results of the project have been used to propose assumptions regarding a strategy for preventing blood-borne diseases in Poland. The research was conducted iteratively using a multi-stage qualitative methodology to explore risk assessment involving blood-borne infections. The final output of the entire project is a list of key problems/challenges and solutions associated with medical and nonmedical services that are connected to the breakage of tissue continuity. Qualitative research is rare in risk assessment as priority is usually given to statistical data and end-points (quantitative studies ). Statistical data on health risk allows to assess the health implications, while the methodology applied also allowed diagnosis of the processes leading to these effects. In addition to policy preparation for blood-borne illnesses, the methodology employed in the study can also be used to successfully explore other areas of public health policy decisions.

This short note proposes a national Geographic Information System (GIS) - based health infrastructure to deal with epidemics and pandemics. Currently, there is no pan-India health infrastructure available that can compile, update, and report the spread of epidemic diseases. It not only curtails the opportunity of finding the real-time data on the spatial distribution of a disease but prevents one to inquire into the causes of the disease through epidemiological analysis. The proposed infrastructure in this study is a pan-India one and can broadly be divided into two parts, hotspot mapping and accessibility to services. In the first part, hospitals are proposed to act as nodes of data collection, sending data to a national GIS portal. This portal shall have the capabilities of plotting the data using map rendering services such as Google and Bing Maps. This way, hotspots can be visualized in no time, benefitting the government and common citizenry alike. The second part deals with the accessibility of citizenry to a wide range of services, i.e., healthcare services, grocery outlets, emergency services, baby food, and many other essential services of the day to day life. In order to implement this, we propose that the government need to enforce a mandatory submission of locational coordinates of all Goods and Services Tax (GST) enrolled service providers. Once the coordinates are submitted, the government can effectively control the opening and closing of services and inform the citizenry at the same time. The proposed infrastructure is going to help deal with the extraordinary situations during epidemic and pandemics similar to what the world is currently facing in the form of Corona Virus Disease 2019 (COVID-19). Furthermore, the infrastructure can be scaled up or down as per the spread of a disease. The health-GIS platform proposed in this concept paper, shall help India in controlling and managing the epidemic more efficiently.

Despite great strides being achieved in improving cancer patients’ outcomes through better therapies and combinatorial treatment, several hurdles still remain due to therapy resistance, cancer recurrence and metastasis. Drug resistance, culminating in relapse and metastatic disease continue to be associated with fatal disease. Cancer stem cells (CSCs) are a subpopulation of cancer cells known to be resistant to therapy and cause metastasis. Whilst the debate on whether CSCs are the origins of the primary tumor rages on, CSCs have been further characterised in many cancers with data illustrating that CSCs display great abilities to self-renew, withstand therapies due to enhanced epithelial to mesenchymal (EMT) properties, enhanced expression of ABC membrane transporters, activation of several survival signaling pathways and increased immune evasion DNA repair mechanisms. CSCs also display great heterogeneity with the consequential lack of specific CSC markers presenting a great challenge to their targeting. In this updated review we re-visit CSCs within the tumor microenvironment (TME) and present novel treatment strategies targeting CSCs. These promising strategies include targeting CSCs-specific properties using small molecule inhibitors, immunotherapy, microRNA mediated inhibitors, epigenetic methods as well as targeting CSC niche-microenvironmental factors and differentiation. Lastly, we present recent clinical trials undertaken to try to turn the tide against cancer by targeting CSC-associated drug resistance and metastasis.

Sharing the same raw material, recycling and composting are in direct conflict with incineration of municipal solid waste in combined heath and power plants. Indeed, waste-to-energy plants in regions with high recycling rates import urban waste from other countries to use otherwise unused capacity, and raise revenues. Using the case of Italy’s second largest and economically most developed region, I discuss the economic viability of municipal solid waste incineration to produce electricity and heath in the context of the increasing role of electricity production from renewable energy sources as well as of the emerging mass-scale uptake of bioplastics. Four lessons and three guidelines aimed to local authorities and policy makers emerge from the present study.

This paper aims to investigate the criteria of behaviour of certain type of third order neutral generalized difference equations with distributed delay. With the technique of generalized Riccati transformation and Philos-type method, some oscillation criteria are obtained to ensure convergence and oscillatory solution of suitable example is listed to illustrate the main result.

Sensory evaluation has been widely applied in assessing the quality of many consumer products that directly serve human needs such as food, beverages, clothing, etc. This paper first examined the inherent deficiencies in this approach, due mainly to the essentially subjective nature of human sensory preference. It then argued that instinctively designating certain materials attributes as sensory perceptions is unnecessary; considering every scientific concept is in essence the processed results of our sensory organs/brains, i.e., all quantities were initially human perceptions. Scientific advances have inevitably generated gradual transformation of such human perceptions as warmth and heaviness into objective parameters measurable in physical quantities like temperature and kilograms. Then using existing successful examples, it demonstrated firmly how the sensory attributes can be assessed by more reliable instrumental methods, and envisioned the key steps to turning a perception into scientifically measurable parameter(s).

Pain is a major problem in sick newborn infants, especially for those needing intensive care. Pharmacological pain relief is the most commonly used but may be ineffective, have side effects, including long-term neurodevelopmental sequelae. The effectiveness and safety of alternative analgesic methods are ambiguous. The objective is to review the effectiveness and safety of non-pharmacological methods of pain relief in newborn infants and to identify those that are the most effective. PubMed and Google Scholar were searched using the terms: ‘infant’, ‘premature’, ‘pain’, ‘acupuncture’, ‘skin to skin contact’, ‘sucrose’ ‘massage’, ‘musical therapy’ and ‘breastfeeding’. We included 24 studies assessing different methods of non-pharmacological analgesic techniques. Most resulted in some degree of analgesia but many were ineffective and some were even detrimental. Sucrose, for example, was often ineffective but more effective than music therapy, massage, breast milk (for extremely premature infants) or non-invasive electrical stimulation acupuncture. There were also conflicting results for acupuncture, skin to skin care and musical therapy. Most non-pharmacological methods of analgesia provide some modicum of relief for preterm infants but none are completely effective and there is no clearly superior method. Study is also required to assess potential long-term consequences of any of these methods.

Aiming at the problem of model error and tracking dependence in the process of intelligent vehicle motion planning, an intelligent vehicle model transfer trajectory planning method based on deep reinforcement learning is proposed, which obtain an effective control action sequence directly. Firstly, an abstract model of the real environment is extracted. On this basis, Deep Deterministic Policy Gradient (DDPG) and vehicle dynamic model are adopted to jointly train a reinforcement learning model, and to decide the optimal intelligent driving maneuver. Secondly, the actual scene is transferred to equivalent virtual abstract scene by transfer model, furthermore, the control action and trajectory sequences are calculated according to trained deep reinforcement learning model. Thirdly, the optimal trajectory sequence is selected according to evaluation function in the real environment. Finally, the results demonstrate that the proposed method can deal with the problem of intelligent vehicle trajectory planning for continuous input and continuous output. The model transfer method improves the model generalization performance. Compared with the traditional trajectory planning, the proposed method output continuous rotation angle control sequence, meanwhile, the lateral control error is also reduced.

Glaucoma is a disease associated with retina of eye. Presently, millions of human being is suffering from this disease. Early detection of these diseases can save the people from blindness. Therefore, various methods have been developed for its detection. In this paper, we have studied the reported methods and summarized their performance in terms of accuracy of detection.

While the mobile phone is the world’s most popular media device, it is actually not one single medium, but is effectively used as a different medium by different user groups. The article characterizes polymodal differences in mobile apps usage among different user groups, including gender, education, occupation, screen size, and price. We monitored the complete app usage of 10,725 smartphone users for one month each (56 million sessions, recording almost 1 million hours). Our key contribution consists in developing and analyzing a theoretical framework to classify the over 16,000 apps used into five categories. Exploring nine research questions we provide a broad characterization by asking who, with which characteristics, uses which kinds of apps in what extensity and intensity? For example, it is not the young and high occupational grades that use the mobile phone as a human-to-machine computer (including gaming and artificial intelligence tools). Large screen size is related to extensive long sessions, while a small screen size is related to intensive frequent usage. The results go beyond providing ample empirical evidence for the inherently polymodal nature of the mobile phone, but also proposes a framework on how to possibility deal with it analytically.

Revitalization of open water bodies into recreational areas are becoming widely popular in many parts of Sri Lanka. Understanding community perception and expectation on recreational development have important policy and cost-effectiveness implications. This paper explores the public perspectives on development, conservation and value of the open water recreational development at Diyatha Uyana and surrounding area. Contingent Valuation Method has been applied to estimate peoples’ Willingness To Pay for recreational spaces in the selected area. A total of 300 residents from different socioeconomic backgrounds were selected randomly for the study. Enjoying peace and relaxation, taking children to play and breath clean air were mentioned as most important aspects of open water body areas. More frequent visits occur to enjoy peace and relaxation, breath clean air and enjoy the natural landscape. Almost all the participants were agreed upon conserving open water body areas in urban settings. Willingness to pay was significantly associated with income and occupation status. It yielded a monthly average payment of Rs.446.93 per household for another five years’ time. The limited budget was the main reason for not willing to pay. This paper is significant as there is no contingent valuation method study has been carried on water body development in Sri Lanka.

BackgroundThe present review summarizes the state-of-the-art of in silico methods and techniques that are the most useful in drug discovery, their relationship with data science, as well as the successful application of data science, machine learning (ML) and artificial intelligence (AI) applications. A meta-analysis of the various technologies available is furthermore proposed as a guideline for the non-expert, reader relative to the several subject areas is also discussed in this article. The scope of this meta-analysis is to rank the enlisted technologies by their field of applications and to depict the latter according to knowledge accessibility, from students to experts.Method The search strategy utilized for this review first produced a general collection of 900 papers without duplications, which were subsequently streamlined and divided into two independent collections: the top 300 most-cited papers of all time (since 2000) and the papers with the highest interest for a systematic review analysis (high-impact exciting papers). Results In Part 1, we discuss the most cited and quality 97 articles in these top 300 papers most relevant to the field of in silico drug discovery. The different disciplines are listed according to their industrial and economic incurred to society, independently from the “metric” results of how many new drug approvals (NDAs) each discipline has generated to date.ConclusionBig data, the ensemble of known items stored in publicly available databases, has improved our understanding of the many fates of a potential drug candidate during its development and even after its commercialization. Moreover, the combination of new screening techniques and “omics” with old drugs has led to a new paradigm in which the unknown knowledge of any biological molecule and its cellular structure, now plays an important role as a target for a series of yet-to-be-developed drugs: the chemical space. Furthermore, leveraging big data, data science, ML, and AI can revolutionize drug discovery by swiftly analyzing massive datasets, predicting efficacy and safety profiles, streamlining development, cutting costs, and boosting success rates for new drugs. AI also speeds up the search for promising drug candidates, advancing innovative therapies.

Addressing non-unions involves stabilizing the affected area through osteosynthesis and improving bone biology using bone grafts. However, there is no consensus on the optimal treatment method. This study aims to compare outcomes of non-union revision using conventional treatment methods (metal hardware ± graft) versus osteosynthesis with the human, allogeneic cortical bone screw (Shark Screw®). Thirty-four patients underwent conventional treatment, while 28 cases received Shark Screws®. Patient demographics, bone healing, time to bone healing, and complications were assessed. Results revealed a healing rate of 96.4% for the Shark Screw® group, compared to 82.3% for the conventionally treated group. The Shark Screw® group exhibited a tendency for faster bone healing (9.4±3.2 vs. 12.9±8.5 weeks, p=0.05061). Hardware irritations led to six metal removals in the conventional group versus two in the Shark Screw® group. The Shark Screw® emerges as a promising option for personalized non-union treatment in the foot, ankle, and select lower leg cases, facilitating effective osteosynthesis and grafting within a single construct, promoting high union rates, low complications, and a rapid healing process.

Crouch gait is one of the most common gait abnormalities. In the literature, there are few works related to the crouch gait recognition. In this work, we aim at the integration of a framework for multiclass crouch gait recognition using synthetic eight-DoF model gait data in anatomical space. The main contribution of our study is the joint function generator algorithm, which could be used to generate other physiological signals; and the gait data validation stage. In addition, we use a feature selection method and a body region segmentation approach to improve classification performance. We generated a gait dataset containing 25 join functions of each DoF for each gait class (4 crouched and normal). The algorithms that we evaluated for classification were: K-nearest neighbors (kNN), Naive Bayes (NB), discriminant analysis (DA), decision trees (DT), and an artificial neural network (ANN). We calculated the metrics: accuracy (Acc), recall (R), specificity (SP), precision (P) and the F-measure (FM) to asses the classification performance. In general, for this work, the best algorithms were discriminant analysis and artificial neural networks. The main results of the proposed recognition framework provide evidence that can be used in real clinical settings to diagnose and make decisions about treatments related to crouch gait diseases.

This paper examines the impact of stringent regulations on personal data protection on customer perception of data security and online shopping behavior. In the context of the rapidly expanding e-commerce landscape, ensuring the security of personal data is a complex and crucial task. The study of several legal frameworks, including Malaysia's compliance with EU regulations and Indonesia's Personal Data Protection Law, provides valuable insights into consumer data protection. The challenges of balancing data safeguarding and unrestricted movement and tackling misuse by external entities are significant and require careful consideration. This research elucidates the pivotal role of trust in e-commerce environments and the deployment of innovative e-commerce models designed to minimize personal data sharing. By integrating advanced privacy-enhancing technologies and adhering to stringent regulatory standards such as the GDPR, this study demonstrates effective strategies for robust data protection. The paper contributes to the academic discourse by providing a comprehensive framework that synergizes legal, technological, and procedural elements to fortify data security and enhance consumer trust in digital marketplaces. This approach aligns with international data protection standards and offers a pragmatic blueprint for achieving sustainable data security in e-commerce.

In the digital age, social media platforms have become a critical channel for brands to engage with their audience and manage their reputation. This paper explores the application of sentiment analysis in social media to aid brand reputation management. Sentiment analysis, a subset of natural language processing (NLP), involves determining the emotional tone behind online discussions and opinions. By leveraging machine learning algorithms and NLP techniques, brands can analyze vast amounts of social media data to gauge public sentiment, identify emerging trends, and detect potential issues before they escalate. This study reviews current methodologies in sentiment analysis, including supervised and unsupervised learning approaches, and discusses their effectiveness in extracting actionable insights from social media content. Additionally, it examines case studies where sentiment analysis has been successfully implemented to enhance brand reputation, address customer concerns, and tailor marketing strategies. The findings highlight the importance of continuous monitoring and adaptation to dynamic social media landscapes for maintaining a positive brand image. This paper provides a comprehensive overview of sentiment analysis tools and techniques, offering practical recommendations for brands seeking to leverage social media sentiment data for proactive reputation management.

Explosion monitoring is performed by infrasound and seismoacoustic sensor networks that are distributed globally, regionally, and locally. However, these networks are unevenly and sparsely distributed, especially in the local scale as maintaining and deploying networks is costly. With increasing interest in smaller yield explosions, the need for more dense networks has increased. To address this issue, we propose using smartphone sensors for explosion detection as they are cost-effective and easy to deploy. Although there are studies using smartphone sensors for explosion detection, the field is still in its infancy and new technologies need to be developed. We applied a machine learning model for explosion detection using smartphone microphones. The data used were from the Smartphone High-explosion Audio Recordings Dataset (SHAReD), a collection of 326 waveforms from 70 high-explosive (HE) events recorded on smartphones, and the ESC-50 dataset, a benchmarking dataset commonly used for environmental sound classification. Two machine learning models were trained and combined into an ensemble model for explosion detection. The resulting ensemble model classified audio signals as either “explosion,” “ambient,” or “other” with true positive rates (recall) greater than 96% for all three categories.

This study offers an in-depth analysis of the COVID-19 pandemic’s trajectory in several member countries of the European Union (EU) to assess similarities in their crisis experiences. We also examine data from the United States to facilitate a larger comparison across continents. We introduce our new approach, which uses a spatio-temporal algorithm to identify five distinct and recurring phases that each country undergoes at different times during the pandemic. These stages include a Comfort Period characterized by minimal COVID-19 activity and limited impacts. The Preventive Situation demonstrates the implementation of proactive measures, with relatively low numbers of cases, deaths, and Intensive Care Unit (ICU) admissions. The Worrying Situation is defined by high levels of concern and preparation as deaths and cases begin to rise and reach substantial levels. The Panic Situation is marked by a high number of deaths relative to the number of cases and a rise in ICU admissions, denoting a critical and alarming period of the pandemic. Finally, the Epidemic Control Situation distinguishes itself by limiting COVID-19 deaths despite a high number of new cases. By examining these phases, we identify the various waves of the pandemic, indicating periods where the health crisis has a significant impact. This comparative analysis highlights the time lags between countries as they transition through these different critical stages and navigate the waves of COVID-19.

Mortality rates in sub-Saharan Africa remains unacceptably high, and disproportionately affecting economically disadvantaged communities. And healthcare delivery which is considered a major contributor to this is challenged by the limited resources and high service costs, prompting many patients to opt for outpatient department services. This study addresses the need for a data-driven approach to healthcare policy formulation by proposing a model for the implementation of a data warehousing system that integrates outpatients into the national healthcare framework. By leveraging data warehousing, healthcare delivery can be made more efficient through a robust repository that supports decision-making and policy formulation. The research focuses on the outpatient department in resource-scarce settings, proposing a novel multidimensional data model and practical framework implementation. The benefits include improved patient experiences, cost-effective service delivery, and enhanced patient management through electronic health records. Additionally, mobile apps are explored for patient navigation and reminders to enhance treatment adherence. The study’s results indicate that data warehousing can significantly improve healthcare delivery by consolidating critical data and facilitating better planning, resource allocation, and policy formulation. Ultimately, this approach offers valuable insights for optimizing healthcare systems in resource-constrained environments, enabling data-driven policies, and improving health outcomes across sub-Saharan Africa.

Nowadays, inquiries are one of the most important tasks to be executed in order to get valued information. When an inquiry is done to gather information about a human behavior there are some problems inherent to it. One of the main problems is how the information about many different persons can be processed to give good information about their environment. Most of the information gathered by these inquiries is used to analyze the behavior of the respondents. It can be used to estimate the success of a certain product on the market or just to validate the satisfaction of current costumers. The veracity of this information can be critical to model environments with some acceptable level of precision. Modelling environments through Artificial Neural Networks (ANN) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in deal with sets of data with some noise, but they are fundamentally surjective mathematical functions and they aren’t able to give different results for the same input. So if an ANN is trained using data where the same input has different images which can be the case of survey data, this can be a problem for the success of modelling the environment. Therefore is necessary to process the data in order to adjust and eliminate invalid data.

This paper will discuss Data, Information, Knowledge, and Wisdom, which is commonly referred to as DIKW. The DIKW Pyramid Model that is often referenced in both academics and practitioner circles will be discussed and shown to be faulty on several levels, including. a lack of definitional agreement. A new DIKW framework will be proposed that focuses on what the DIKW elements do in the way humans think, not what they are by definition. Information as a replacement for wasted physical resources in goal-oriented tasks will be a central organizing point. The paper will move the DIKW discussion to the computer-based concept of Digital Twins (DTs) and its augmentation of how we can use DIKW to be more effective and efficient. This will especially be the case as we move to Intelligent Digital Twins (IDT) with Artificial Intelligence (AI).

OBJECTIVE: Analyzing the effects of different physical exercise programs on the anthropometric, cardiovascular, metabolic, and strength variables of the elderly participating in health care programs. METHODS: Controlled clinical trial, with 60 elderly participants from health care groups, allocated into four groups: Group of resistance exercises in open-air gyms – GT1 (n = 17); Group of aerobic and localized exercises - GT2 (n = 11); Group of resistance exercises at GT3 gym (n = 17); Control group: non-exercise practitioners - CG (n = 15). Anthropometric (BMI, % body fat, and waist circumference), cardiovascular (SBP, DBP, HR, and DP), metabolic (total cholesterol, triglycerides, and glycemia), and strength variables were evaluated before and after 16 weeks of intervention. Descriptive statistics, Shapiro-Wilk and Kolmogorov-Smirnov normality test, equal variance test, T-Student test, non-parametric Mann-Witney test, ANOVA One Way, multivariate data analysis using data mining and machine learning techniques, Pearson and Spearman correlation tests, Classical Clustering (Agglomerative Hierarchical Method); Principal Component Analysis (PCA), Z score, Fruchterman-Reingold algorithm, Euclidian Similarity Index and, Cohen’s equations were applied. RESULTS: To observe effect size, morphofunctional variables for the GT1 group show a small effect for fat percentage, WHR, and ULS and a medium effect for LLS. For the GT2 group, there was a small effect for fat percentage, WHR, and ULS and a large effect for LLS. For the GT3 group, there was a small effect for the percentage of fat and HC, a medium effect for ULS, and a large effect for LLS. A small effect for glycemia in GT1, a medium effect for glycemia and triglycerides for GT2, a small effect for total cholesterol for GT3, and a large effect for glycemia in CG, with this effect being an increase in this analyte. For the cardiovascular variables, there is a small effect for SBP and HR in GT1, a small effect for DBP, HR, and DP, and a large effect for SBP in GT2, a small effect for SBP, DBP, and HR in GT3 and small effects for SBP and HR in CG. The correlation between BMI (P = 0.0007) and Body Fat% (P = 0.007) with ULS. The variables TG, LLS, and ULS were the ones that differed most in each type of stimulus chosen as an intervention in the present study. CONCLUSIONS: The results of this study indicate that GT2 caused more significant percentage reductions in body mass and BMI. However, GT3 caused greater fat percentage reduction. The GT2 caused the greatest decrease in waist circumference, while the GT3 caused the largest decline in hip circumference. The three groups induced an increase in strength (ULS and LLS). However, in ascending order, GT1 caused the smallest increase, GT2 an intermediate increase, and GT3 the greatest increase in strength, with considerable effects in GT2 and GT3. Finally, GT2 caused a greater percentage reduction in systolic blood pressure (large effect) and double product (small effect) than the other groups. The three methods proved to be efficient, but with particularities that may reflect the choice of one over another due to health conditions, objectives to be achieved, or characteristics of the patient at the time of their choice by the prescriber.

Multiple imputation with chained equations (MICE) is a widespread approach to account for missing data in empirical research. Combining MICE with bootstrapping, that is, repeatedly resampling with replacement from the data to estimate variances and confidence intervals of statistics of interest, is not straightforward. The current document provides an overview of how to use bootstrapping with imputed data in Stata. Two main approaches (impute first and then bootstrap or vice-versa) are discussed and shortly compared. Code is provided for Stata.

With the development of electricity spot markets, accurate electricity load forecasting enables power generation companies to supply the right amount of electricity, Significantly avoid power waste. As a result, time series forecasting in the field of power can bring significant benefits. Previously, the Informer model successfully introduced the Transformer into long time series forecasting(LTSF) by proposing the ProbSparse self-attention mechanism, which solved the inherent problem of high memory complexity in self-attention. Recent research has further demonstrated the potential of the self-attention for mining complex dependencies. However, the limited amount of historical data has become one of the main challenges in applying deep learning techniques to power LSTF tasks. Previous reserches often add a large number of time covariates to provide more information. In this paper, to address this issue, (i) we design a simple but effective Season-aware Block to enhance the model’s ability to mine artificial prior information in temporal covariates; (ii) we conduct experiments using the provincial power data of Zhejiang Province, China, from 2019 to 2022, and our model outperforms other models, achieving a 19 percent MSE relative improvement; (iii) we conduct ablation experiments to assess the efficacy of the Season-aware Block in extracting temporal periodic features. Furthermore, we elucidate the underlying reasons for the effectiveness of both the self-attention mechanism and the Season-aware Block through visualization experiments.

1. Background Septic shock is a rare and serious infectious disease. Patients surviving the early phase are at high risk of multiple amputations of limbs. They are potential sources of serious disability. 2. Methods The objective was to describe the functional prognosis and quality of life of the four-limb amputee patients following septic shock. This is a descriptive, retrospective and mono-centric study. The patients included between 2010 and 2019 had septic shock after 18 years, responsible for amputation of the four limbs. We studied the mean age of septic shock, the causative organisms, the infectious door enter, the different levels of amputation, the fitting process, the resumption of walking, the Functional Independence Measure (FIM) and the SF-12 Quality of Life Questionnaire. 3. Results We suceed to include 15 patients.. 53.3% women and 46.7% men with an average age of 46.1 years. 80% presented bilateral trans-tibial amputations, all were able to return walking even over short distances, within an average of 8.4 months. 40% did not require a technical walking aid, 33.3% had no walking perimeter limitation. 47% had a higher mental and social quality of life score than the general population. The younger the patients, the better their physical quality of life. After two years of follow-up, the younger the patients, the better their functional autonomy. Patients with better functional autonomy had a better physical quality of life. 4. Conclusion The number of amputations due to spetic shock is likely to increase in the years to come throughout the world. Our study prove that quadri-amputee patient can regain a fully acceptable functional and quality of life being monitored in a specialized health center with multidisciplinary health professional to help him.

The objective was to estimate the correlation between VIs and grain yield and identify the optimal timing and VIs for precise corn grain yield estimation. Furthermore, the study aims to employ photographic quantification to measure corn ear traits and establish their correlation with corn grain yield. Ten corn hybrids were evaluated in CRB with three rep-lications at three locations. Vegetation indices and green leaf area were estimated throughout the cycle using an unmanned aerial vehicle (UAV) and subsequently corre-lated with grain productivity. In addition, photographs were taken of the corn ear to esti-mate their length, width and total number of kernels and compare these values with manual measurements. The experiments consistently demonstrated significant experi-mental quality across sites, with accuracy ranging from 79.07% to 95.94%. UAV flights carried out at the beginning of the crop cycle revealed a positive correlation between grain productivity and the evaluated indices (NGRDI, VARI, GLI). Regarding the phenotyping of corn ears, the regression coefficients for width, length and TNG were 0.92, 0.88 and 0.62, respectively, indicating an association with manual measurements. However, stage V5 in the localities of Lavras and Ijaci and stage V8 in the locality of Nazareno showed a posi-tive correlation with productivity. The use of images for ear phenotyping is promising as a method for measuring corn components.

Satellite-derived soil moisture observations typically rely on bias-correction (BC) prior to assimilation in land surface models. Current techniques include rescaling or machine learning approaches to map the observations to the modelled soil moisture climatology. However, these approaches do not allow for non-stationary biases and recalibrations require a long training period, which is not always feasible. In this study we evaluate a two-stage filter to dynamically correct soil moisture biases from satellite-derived active ASCAT C-band and passive L-band SMOS surface soil moisture observations in the European Centre for Medium Range Weather Forecasts (ECMWF) land data assimilation system. This adaptive soil moisture BC approach is designed to complement the operational seasonal rescaling of the ASCAT observations and the SMOS neural network retrieval used at ECMWF, while allowing the assimilation to correct sub-seasonal scale errors. Over a 3-year test period, the adaptive BC reduces the seasonal-scale first guess-observation departures by 20-30% for ASCAT and SMOS. The adaptive BC leads to (i) slight improvements in soil moisture performance against in situ data; and (ii) moderate but statistically significant reductions in the 1-5 day relative humidity forecast errors in the boundary layer of the northern hemisphere midlatitudes.

Keywords: AI Readiness Assessment; Business Processes; and Data Infrastructure.

The graph model enables a broad range of analysis, thus graph processing is an invaluable tool in data analytics. At the heart of every graph processing system lies a concurrent graph data structure that stores the graph. Such a data structure needs to be highly efficient for both graph algorithms and queries. Due to the continuous evolution, the sparsity, and the scale-free nature of real-world graphs, graph processing systems face the challenge of providing an appropriate graph data structure that enables both fast analytical workloads and low-memory fast graph mutations. Existing graph structures offer a hard tradeoff between read-only performance, update friendliness, and memory consumption upon updates. In this paper, we introduce CSR++, a new graph data structure that removes these tradeoffs and enables both fast read-only analytics and quick and memory-friendly mutations. CSR++ combines ideas from CSR, the fastest read-only data structure, and adjacency lists to achieve the best of both worlds. We compare CSR++ to CSR, adjacency lists from the Boost Graph Library, as well as state-of-the-art update-friendly graph structures: LLAMA, STINGER, GraphOne, and Teseo. In our evaluation, which is based on popular graph processing algorithms executed over real-world graphs, we show that CSR++ remains close to CSR in read-only concurrent performance (within 10% on average), while significantly outperforming CSR (by an order of magnitude) and LLAMA (by almost 2×) with frequent updates. We also show that both CSR++’s update throughput and analytics performance exceed that of several state-of-the-art graph structures, while maintaining low memory consumption when the workload includes updates.

The performance of 5 forecasting models was investigated for predicting nursery mortality using the master table built for 3,242 groups of pigs (~ 13 million animals) and 42 variables, which concerned the pre-weaning phase of production and conditions at placement in growing sites. After training and testing each model’s performance through cross-validation, the model with the best overall prediction results was the Support Vector Machine model in terms of Root Mean Squared Error (RMSE=0.406), Mean Absolute Error (MAE=0.284), and Coefficient of Determination (R2=0.731). Subsequently, the forecasting performance of the SVM model was tested on a new dataset containing 72 new groups, simulating ongoing and near real-time forecasting analysis. Despite a decrease in R2 values on the new dataset (R2=0.554), the model demonstrated high accuracy (77.78%) for predicting groups with high (5&gt;%) or low (5&lt;%) nursery mortality. This study demonstrated the capability of forecasting models to predict the nursery mortality of commercial groups of pigs using pre-weaning information and stocking conditions variables collected post-placement in nursery sites.