Artificial intelligence (AI) has emerged as a powerful tool for addressing the challenges posed by climate change and their impact on public health. By leveraging its capacity to analyze and predict climatic patterns, AI offers opportunities to enhance resource management and develop effective strategies for climate change mitigation. Moreover, AI can contribute to generating sustainable solutions that address the complex and interconnected nature of climate change. For example, AI can enable the optimization of energy consumption and facilitate the integration of renewable energy sources into existing systems. It can also support the development of climate models that provide timely and accurate predictions, enabling policymakers to implement proactive measures for disaster preparedness and response. Furthermore, AI-powered disease surveillance and mitigation techniques can improve public health outcomes by identifying patterns and trends in the spread of diseases in relation to climatic factors.However, the widespread adoption of AI-based solutions is not without challenges. Ethical concerns surrounding privacy and data ownership must be addressed, as the use of AI requires access to large datasets, raising potential privacy risks. Technical constraints, such as limited computational power and the need for sophisticated algorithms, also pose obstacles to the implementation of AI in climate change mitigation strategies. Furthermore, issues related to the accessibility and affordability of AI technologies must be resolved to ensure equitable distribution and maximize its potential impact on public health.To fully harness the power of AI in addressing climate change and improving public health outcomes, it is crucial to promote innovation, multidisciplinary collaboration, and open data science. Innovation can drive the development of new AI algorithms and technologies specifically tailored to address climate change challenges. Multidisciplinary approaches that bring together experts from diverse fields, including climate science, public health, and computer science, can foster a holistic understanding of complex systems and enable the design of comprehensive solutions. Finally, open data science practices, such as sharing data and algorithms, can facilitate collaboration and accelerate progress in mitigating climate change and its public health impacts.In conclusion, AI offers promising opportunities for effectively addressing climate change and its impact on public health. However, to fully realize its potential, it is essential to tackle ethical and privacy concerns, overcome technical constraints, and ensure accessibility and affordability. By promoting innovation, multidisciplinary collaboration, and open data science, we can unlock the transformative power of AI in mitigating climate change and improving public health outcomes.

Prostate cancer is the second most frequent malignancy (after lung cancer) in men worldwide. Prostate tissue biopsies are usually graded using scores according to the Gleason grading system. This Gleason score is the most popular prognostic marker that reveals the potential aggressiveness of the disease. However, inter-observer variability in rating by human assessors is a major limiting factor. Such variability could lead to missing a severe case or suggesting unnecessary treatments. This study explores the discriminative ability of artificial intelligence (deep learning models) for Gleason score assessment. The study was designed to use whole-slide images of digitized H&E-stained biopsies of prostate tissues to automate the grading process and provide a remotely accessible clinical decision support system. Custom convolutional neural network architectures were trained on 10,616 images of prostate tissues. Gaussian filters were applied to pre-process the images and improve model performance. Transfer learning was applied to train eight machine learning architectures namely: Xception, VGG16 & 19, ResNet101, MobileNet, DenseNet121, EfficientNetB5 & B7. Efficient NetB7 had the best performance 85.2% compared with ground-truth classification by experts. Performance improves as more data is available. The model was deployed and hosted as a web application API on Google cloud service to ensure remote access. Tissue biopsy images can be uploaded and the corresponding Gleason score recovered immediately. This system reduces diagnostics turnaround time, increase throughput, and compensate for limited skills, especially in low resource settings.

This paper shows the firsts results of a methodological approach in which Remote Sensing technologies (Satellite imagery and LiDAR) combined with Least Cost Path algorithm on GIS are being tested like tools to overcome locally-based interpretations about populational patterns and communication routes during Flavian municipality in the Comarca de El Condado region. The identification of fourteen hypothetical communication routes between already known archaeological sites thanks to a recent archaeological prospection, and the confirmation of three sections of them and a new archaeological site demonstrate that the combined use of the aforementioned technics has a very useful to delve more deeply, more widely and at a variety of different scales into the relation between the twelve known/selected Early Roman Empire sites and the surrounding landscape in the right bank of Guadalén River. It is expected that, in next phases, this methodological approach will allow us to go deeper in analysing and understanding spatial distribution, exploitation, and interrelation between different Early Roman Empire sites, helping us to fill the aforementioned gaps in our understanding of the archaeological continuum in the region reducing time and costs.

Rising demand and urbanization have led to the global increase and intensification of livestock production. Intensive production has harmful effects on the environment, public health (due to the emergence of zoonotic diseases), and animal welfare. In order to better understand how livestock production intensifies as countries develop and incomes rise, we examine the relationship between income per capita and intensification of poultry and cattle, using the states of India as a study region. We also replicate this analysis at the district level to test whether the relationship holds at different spatial scales. We identify outlying states and districts to see if there are policies that can keep agriculture production largely extensive, even as economic growth occurs. The results support the relationship of income being positively correlated with the proportion of livestock in intensive conditions. However, there are notable outlier states which have grown wealthy while still maintaining largely extensive production, indicating that there may be some cases where economic growth can occur without intensification.

The emerging industry of offshore wind turbines mounted on floating bases has garnered significant attention from both academia and industry. The desire to understand the complex physics of these floating structures has led to the development of numerical and physical modelling techniques. While physical testing has traditionally been employed, there is a growing focus on cost-effective and accurate high-fidelity numerical modelling as a potential alternative or supplement. However, commonly used numerical engineering tools in the offshore industry are considered mid- to low-fidelity and may lack the desired precision for floating offshore wind turbines (FOWTs). Given the complexity of these simulation codes, it is crucial to validate their accuracy. To address this, the International Energy Agency (IEA) Wind Technology Collaboration Programme initiated various research endeavors, including the Offshore Code Comparison Collaboration (OC3), Offshore Code Comparison Collaboration Continuation (OC4), Offshore Code Comparison Collaboration Continuation with Correlation (OC5), and the recent Offshore Code Comparison Collaboration Continued with Correlation and Uncertainty (OC6) projects. This study offers a comprehensive survey of the simulation tools available for FOWTs which were part of OC projects, focusing particularly on horizontal axis wind turbines (HAWTs) and highlighting their capabilities and fundamental theories.

Nanoindentation load-depth (P-h) plots is the basis for determination of mechanical properties of the samples and is especially vital for thin coatings deposited on substrates of various mechanical makeup from amorphous-brittle (borosilicate glass) to Silicon (having different crystallographic texture) to ductile substrates like stainless steel as well other aspects like indenter tip shape. The elastic-plastic response by simulated P-h plots as per modified empirical relations have been presented. The outcomes of this computational studies enlighten the database on the P-h plots for various coating/substrate systems and shall aid in the analysis of experimental P-h plots obtained,

We show succinctly a natural origin of the beautiful Gamma function in light of the WJ distribution, linking to quite distinct fields.

Modern research applies the Open Science approach that fosters the production and sharing of Open Data according to the FAIR (Findable, Accessible, Interoperable, Reusable) principles. In the geospatial context this is generally achieved through the setup of OGC Web services that implements open standards that satisfies the FAIR requirements. Nevertheless, the requirement of Findability is not fully satisfied by those services since there’s no use of persistent identifiers and no guarantee that the same dataset used for a study can be immutably accessed in a later period: a fact that hinders the replicability of research. This is particularly true in recent years where data-driven research and technological advances have boosted frequent updates of datasets. Here, we review needs and practices, supported by some real case examples, on frequent data or metadata updates in geo-datasets of different data types. Additionally we assess the currently available tools that support data versioning for databases, files and log-structured tables. Finally we discuss challenges and opportunities to enable geospatial web services that are fully FAIR: a fact that would provide, due to the massive use and increasing availability of geospatial data, a great push toward open science compliance with ultimately impacts on the science transparency and credibility.

Star copolymer films were studied for confirming the role of film morphology in antimicrobial activity previously reported. With this scope, films of a two-branches copolymer, namely m-PEG-(MMA-ran-DMAEMA)2, containing ≈ 40 % in mol of non-quaternized 2-(dimethylamino)ethyl methacrylate, were produced by using spin-coating, drop-casting and casting deposition techniques, thus obtaining ultra-thin, thin and thick films, respectively. The morphology is generally flat for thin and thick films, but it becomes substrate dependent for ultra-thin films where the film planarization effect is not efficient. Mechanical properties of such films were investigated by Force Volume Maps in both air and liquid. In air, ultra-thin films are in the substrate-dominated zone and thus the elastic modulus E is overestimated, while E reaches its bulk value for thin and thick films. In liquid (water), E follows an exponential decay for all films with a minimum soaked time t0 of 0.37 and 2.65 h for ultra-thin and thin and thick films, respectively. After this time, E saturates to a value in average 92 % smaller than that measured in air due to films swelling. Such results confirm the role of morphology envisaged in literature, suggesting also an additional role of mechanical properties in the antimicrobial activity.

Traditional methods of non-metallic materials joining for industrial uses are limited to lasers, or induction or conduction heating. The precision and efficiency for large-scale applications are difficult to be achieved at the same time. Here in this paper, we report a precise microwave welding method induced by localized surface plasmon of metallic macro-gaps under microwave excitation. The proposed method utilizes energy from well-developed commercial microwaver, and benefits from the precision of localized surface plasmon, an oscillating electron cloud excited by electromagnetic waves, which is currently under hot investigation only at micro- or nano-scales. The method is applied to the welding of polymers to solve the problems of complex processing equipment and high processing energy consumption in the traditional welding process, so as to cope with the growing energy and environmental crisis. Finite element numerical simulations are used to study the distribution characteristics of electric fields and temperature fields. The results show that the process is a high quality, efficient, low consumption and environmentally friendly green process compared to traditional polymer welding methods.

Coral reefs are one of the most productive yet vulnerable ecosystems on Earth. An important step in understanding the functioning of coral communities is studying the environmental impact on the state of reefs. The present study aimed to assess the impacts of the water temperature, precipitation, wave action and population density on the seasonal bleaching, partial and complete mortality of shallow-water branching corals of Pocillopora verrucosa species complex. The present study was conducted at the Dam Bay research station (Nha Trang Bay) from 26 April 2020 to 2 April 2021. Throughout the study the percentage of bleached coral colonies varied from 1 to 41.5% with two peaks, in autumn (October) and spring (April). Rates of the development of partial mortality varied from 0 to 10%, with a pronounced peak in December. At the end of the experiment, the percentage of coral colonies suffering from partial mortality reached 47%. Only 4,5% of experimental colonies died by the end of the study. Analysis revealed that water temperature and partial mortality are the drivers of bleaching, while wave action, temperature and bleaching are the factors with a significant impact on partial mortality. In the present study, the main cause of coral death was the development of algal fouling at the sites of colony lesions, which gradually spread over the entire surface of the colony. We showed also that the impact of individual stress on mortality prevailed over the impact of seasonal bleaching and partial mortality events.

The possibility of a shock wave recovery at a discrete closed interface with a heated gas has been investigated. A two-dimensional model applied to conditions of optical discharges featuring spherical, elliptical, and drop-like configurations demonstrated that non-symmetry in the shock refraction contributes to the specific mechanism of recovery other than simply its compensation. Even though the full restoration of the hypersonic flow state doesn’t occur in a strict sense of it, clear reverse changes toward the initial shape of the shock front eventually take place, thus creating an appearance of a full recovery seen in experiments. From analysis for different interfaces symmetries, the factors determining the recovery dynamics are identified. The results are directly applicable to the problem of energy deposition into a hypersonic flow, however it can be useful anywhere else where the flow modifications following the interaction are important. The dimensionless form of the equations allows applications on any scale other than that demonstrated for the optical discharges.

This study addresses the prevalent gap between formal and informal architectural methodologies in software engineering. Recognizing the potential of informal architecture artifacts in analytical processes, we introduce a groundbreaking methodology that efficiently transforms these informal components into structured formal models. This method facilitates a deeper understanding and utilization of informal diagrams and enhances analytical capabilities through graph analysis techniques. By leveraging user-friendly tools like Draw.io, the methodology democratizes the modeling process, making sophisticated architectural analyses accessible to a broader spectrum of professionals without requiring deep expertise in formal methods. The innovative aspects of this methodology lie in its ability to streamline the transformation process, significantly improving both the efficiency and effectiveness of model creation and analysis. These enhancements are demonstrated through a practical application involving a sample architecture diagram, where the resulting model is thoroughly analyzed using advanced graph analysis tools like Python's NetworkX library and Neo4j. This approach bridges the theoretical and practical divides in software architecture and sets a new standard for integrating informal artifacts into systematic engineering workflows. In addition, considerations for Artificial Intelligence developments are discussed.

The primary objective of this research was to assess the energy consumption of the gas treatment units at Bonny NLNG Refinery, the first Gas Refinery in Rivers State, Nigeria, while utilizing semi-lean amine. To achieve this goal, a simulation of the units was conducted using the advanced software package Aspen Hysys (V.8.3). The simulation was designed to accurately represent the dynamic behavior of the refinery's gas treatment units, allowing for a comprehensive analysis of their energy usage. The research aimed to quantify the energy consumption of the gas treatment units and identify opportunities for energy optimization. By utilizing semi-lean amine, which is known to improve energy efficiency, the study sought to evaluate the potential energy savings that could be achieved in the refinery's operations. The simulation model incorporated the specific design and operational parameters of the gas treatment units at Bonny NLNG Refinery, including the gas flow rate, lean amine concentration, absorber pressure, stripping temperature, amine circulation rate, and acid gas removal efficiency. By considering these parameters, the simulation accurately represented the dynamic behavior of the gas treatment units, enabling a detailed analysis of their energy consumption. Through the simulation, various scenarios and operational conditions were evaluated to determine the optimal set of parameters that minimized energy consumption. The research also examined the trade-offs between energy consumption, acid gas removal efficiency, and other performance indicators, such as amine circulation rate and regeneration efficiency. The findings of this research have significant implications for the energy efficiency and sustainability of gas treatment operations at Bonny NLNG Refinery. By identifying opportunities for energy optimization and providing recommendations for the utilization of semi-lean amine, the study contributes to the development of more efficient and environmentally friendly gas treatment processes. Overall, this research combines advanced simulation techniques with a comprehensive analysis of energy consumption to provide valuable insights into the energy efficiency of gas treatment units at Bonny NLNG Refinery, enabling informed decision-making and potential improvements in energy utilization. By utilizing the percentage-based unit simulation approach, a detailed examination of the energy consumption patterns was obtained. This analysis provides valuable insights into the operational efficiency and determination of potential energy-saving opportunities within the gas treatment units. This study specifically focuses on the integration of an absorption column split stream (stream flow) and a flash unit as a potential means to reduce the energy consumption of gas treatment devices. The integration of these units aims to optimize the overall energy efficiency of the gas treatment process by recovering and utilizing waste heat and reducing energy losses. The absorption column split stream allows for the diversion of a portion of the gas stream to a flash unit before entering the absorber. The flash unit operates at a lower pressure, which facilitates the release of entrained hydrocarbons and reduces the overall energy requirements for gas treatment. By separating and recovering the hydrocarbons in the flash unit, energy losses associated with their absorption and subsequent regeneration are minimized. Through the simulation, the study analyzes the energy consumption patterns of the gas treatment units with and without the integration of the absorption column split stream and flash unit. Comparative assessments are conducted to evaluate the energy savings and overall operational efficiency achieved through this integration. The findings of this research will provide valuable insights into the potential energy-saving opportunities offered by the integration of the absorption column split stream and flash unit in gas treatment devices. It will help refine the design and operation of gas treatment units, enabling more energy-efficient processes in the gas refining industry. By optimizing energy consumption in gas treatment units, the study contributes to the industry's goals of reducing greenhouse gas emissions and improving sustainability. The integration of the absorption column split stream and flash unit offers a promising approach to enhance energy efficiency, reduce operational costs, and minimize environmental impact. Overall, this research underscores the importance of exploring innovative solutions, such as the integration of different process units, to achieve energy savings in gas treatment operations. Through a comprehensive analysis of energy consumption patterns, this study aims to provide practical recommendations for optimizing energy efficiency in gas treatment units, fostering a more sustainable and efficient gas refining industry.The research specifically explores the impact of integrating the absorption column split stream and flash unit when dealing with sour gas streams containing carbon dioxide concentrations of less than mole ℅, while utilizing MDEA fluid as the solvent. Through comprehensive analysis and simulation using advanced software tools, the study demonstrates that by incorporating the absorption column split stream and flash unit, a significant reduction in device energy consumption of up to 10% can be achieved. The integration of the absorption column split stream and flash unit offers promising opportunities to enhance the energy efficiency of gas treatment devices. By diverting a portion of the gas stream to the flash unit, the separation of hydrocarbons and the subsequent release of entrained hydrocarbons at lower pressure significantly reduce the overall energy requirements for gas treatment. The comprehensive analysis and simulation conducted in the research provide concrete evidence of the energy-saving potential of this integration. By quantifying the energy consumption patterns and comparing scenarios with and without integration, the study establishes the effectiveness of the approach and its impact on operational efficiency. The findings of the research highlight the importance of considering the specific gas composition and utilizing appropriate solvents, such as MDEA, for optimizing energy efficiency. The integration of the absorption column split stream and flash unit not only reduces energy consumption but also offers the potential for cost savings and environmental benefits. Overall, the research demonstrates that by incorporating the absorption column split stream

Precision medicine approaches often relies on complex and integrative analysis of multiple biomarkers from “omics” data to generate insights that can help either diagnostics, prognostics or therapeutical decisions. Such insights are often made using Machine learning (ML) models that make sample classification for a particular phenotype (yes/no). Building such models is a challenge and time-consuming, requiring advanced coding skills and mathematical modelling expertise. Artificial intelligence (AI) is a methodological solution that has the potential to facilitate, optimize and scale model development. In this work, we developed an AI-based, user-friendly and code-free platform (https://digitalphenomics.com) that fully automates the development of predictive models from quantitative “omics” data. Here, we show the application of this tool with the development of cancer survival prognostics models using real-life data from breast, lung and renal cancer transcriptomes. We report and compare their sensitivity, specificity, accuracy and Receiver Operating Characteristic (ROC) curve Area Under the Curve (AUC). Further, we report the associated sets of genes (biomarkers) and their expression pattern that are predictive of cancer survival. Moreover, we made our models available as online tools to generate prognostic predictions based on the gene expression of the biomarkers. In conclusion, we demonstrated that our tool is a robust user-friendly solution to develop bespoke predictive tools from “omics” data which facilitate precision medicine introduction to the point-of-care.

Security in IoT systems is extremely important, as an intrusion into an IoT device or network can affect not only our domestic lives, but also industrial assets, with the potential to cause enormous damage. We discuss IoT security issues as defined by the OWASP Foundation, focusing on network related aspects. After a brief description of SDN in general and OpenFlow in particular, we discuss how SDN technologies can greatly help in designing and deploying more secure IoT networks by enhancing the cryptographic capabilities of devices, isolating devices or networks, blocking access to unwanted services, redirecting traffic to deep inspection systems, monitoring packet flows and devices, etc. These capabilities can be implemented using open-source OpenFlow controllers such as Faucet.

This survey proposed an evaluation model to analyze and examine different approaches to generativity. In addition to problem domain concepts, the following concepts were used to define this model: Complexity and complexity management, and Systematics view to achieve unified and integrated insight into disparate evaluation criteria. The research's approach to the said concepts is first introduced. Then, the evaluation model is presented.

This paper presents the validation of the End Point Rate (EPR) tool for QGIS (EPR4Q), a tool built-in QGIS Graphical Modeler to calculate the shoreline change by End Point Rate method. The EPR4Q tries to fill the gap of user-friendly and free open-source tool for shoreline analysis in Geographic Information System environment, since the most used software - Digital Shoreline Analysis System (DSAS) - although is a free extension, is suited for commercial software. Besides, the best free open-source option to calculate EPR called Analyzing Moving Boundaries Using R (AMBUR), since it is a robust and powerful tool, the complexity and heavy processes can restrict the accessibility and simple usage. The validation methodology consists of applying the EPR4Q, DSAS, and AMBUR on different examples of shorelines found in nature, extracted from the U.S. Geological Survey Open-File. The obtained results of each tool were compared with Pearson correlation coefficient. The validation results indicate that the EPR4Q tool created acquired high correlation values with DSAS and AMBUR, reaching a coefficient of 0.98 to 1.00 on linear, extensive, and non-extensive shorelines, guarantying that the EPR4Q tool is ready to be freely used by the academic, scientific, engineering, and coastal managers communities worldwide.

Hands-on type training of Integrated Computational Materials Engineering (ICME) is characterized by assisted application and combination of multiple simulation software tools and data. In this paper, we present recent experiences in establishing a cloud-based infrastructure to enable remote use of dedicated commercial and open access simulation tools during an interactive on-line training event. In the first part, we summarize the hardware and software requirements and illustrate how these have been met using cloud hardware services, a simulation platform environment, a suitable communication channel, common workspaces and more. The second part of the article focuses (i) on the requirements for suitable on-line hands-on training material and (ii) on details of some of the approaches taken. Eventually, the practical experiences made during three consecutive on-line training courses held in September 2020 with 35 nominal participants each, are discussed in detail.

VANET networks are a class of peer-to-peer wireless networks that are used to organize communication between cars (V2V), cars and infrastructure (V2I) and between cars and other types of nodes (V2X). These networks are based on the DSRC, 802.11 standards and are mainly intended for organizing the exchange of various types of messages, mainly emergency ones, to prevent road accidents or alert when road accident occur, or control the priority of the driveway. Initially it was assumed that cars would only interact with each other, but later, with the advent of the concept of Internet of things (IoT). Researchers began to analyze connectivity with other devices, which in general will allow to combine various road users and other devices that can used in the creation of intelligent transport infrastructure in a single smart city management system. Infrastructure is necessary for the provision of services, monitoring and management of the VANET network. As infrastructure objects it is proposed to use stationary objects of Roadside unit (RSU). The aim of this paper is to analyze the use of mobile edge computing to decrease the load to the base station and latency between RSU clouds and provide a real experiment using software defined networking and mobile edge computing for RSU.

The procedures used to create modern cars require extensive thought in various relevant scientific domains. Arguably, the most challenging obstacle facing the automobile sector is managing production facilities by integrating software production lines and CI/CD. All this is determined by market demands, the engine of a vehicle, and the complexity of assembling the entire car and installing its corresponding embedded software. As a result, concerns about various types of global change have grown, as well as the lack of the ability to use fossil fuels, creating a substantial impact on the purchase and sale of modern automobiles. The research foundation is reflected in covering strategies for the deployment and administration of software, as well as opportunities for business improvement in particular production processes. This article strives to provide a summary of a scientific investigation of original equipment manufacturers, market segmentation, and the effects of global market changes on automotive manufacturing by examining the correlation between certain changes in the purchase of a specific brand and the powertrain of a vehicle. The research tries to examine numerous datasets from the United States of America and Washington State, on the basis of which we may estimate possible future changes in the automotive industry’s sales.

Integrating systems acquired from third parties and legacies has become of great necessity in companies. This is mainly due to the need to exchange information between entities, such as banks, suppliers, customers, allies, etc. Therefore, it is essential to guarantee the integrity of the data and keep the integration up to date with the changes that can occur. Furthermore, reducing the transaction risk and avoiding losing information is necessary. Implementing this complex task involving technological and business challenges requires appropriate architecture implementation. This article presents an architecture named Dynamic Canonical Data Model through agnostic messages. The contribution involves treating the low link in integrating software units, also called the integration of loosely coupled software units. In particular, it focuses on internal data and external data integration. The proposal reduces their integration, maintenance time, and cost, maximizing their scalability and promoting reuse. To illustrate their components, a case study undertaken at the Mexican Logistics Company Paquetexpress is presented. The company has evaluated different enterprise application integration systems over 26 years to achieve a standardized integration. The article describes an architecture designed and implemented for this delivery company in a real-world case. The implementation’s source code was registered in the National Registry of Copyrights of Mexico.

Scope, time, and cost permanently effects each other and most of Information Technology projects fails due to these three factors. Scope shifting mostly occur due to time and cost. At project start, lack of understanding of project and product scope is focal involvement that leads to unsuccessful projects. Complete software scope definition determines quality of project. Defining the customer requirement and the definite scope of project has key role for implementation of project management. The complications originates when systems are developed from impractical expectations and misunderstanding requirements. These problems are cause of many changes, occurs in system development and leads to poor scope management. Scope creep is one of the momentous prompting parameter on the success of project. The failure in manage scope creep leads for 80 percent of software projects failure. However, using agile approach the impact of scope creep on projects become insignificant. A correctly distinct scope tends us to develop a quality product, within identified plans and decided cost to the stake-holders.

This paper presents a method for a decentralised peer-to-peer software license validation system using cryptocurrency blockchain technology to ameliorate software piracy, and to provide a mechanism for software developers to protect copyrighted works. Protecting software copyright has been an issue since the late 1970's and software license validation has been a primary method employed in an attempt to minimise software piracy and protect software copyright. The method described creates an ecosystem in which the rights and privileges of participants are observed.

With the trend of public infrastructure becoming more sustainable, large multi-purpose sports venues (LMPSVs) have become one of the world's most complex architectural projects and one of the landmark buildings of an international city. LMPSVs will lead the world in sustainable design, construction and operational innovations, and play a crucial role in raising awareness of sustainability around the world. System Thinking (ST), System Dynamics (SD) and Sustainable System Development (SSD) theories are three interrelated concepts that together form a methodological framework for solving complex system problems and driving strategies. The purpose of this study is to examine how LMPSVs can implement environmental, social, governance, and innovation (ESGI) enterprise transformation strategy (ETS) based on a sustainable systems development (SSD) framework. In view of the current global environmental challenges and social responsibility requirements, the motivation of this study is to provide an integrated transformation pathway for LMPSVs to achieve the goal of sustainable development and to contribute to the field of related disciplines. The methodology of this research adopts a combination of literature review and case study analysis, and the theoretical basis is mainly drawn from cutting-edge research in the fields of sustainable development, corporate social responsibility (CSR) and environmental management. Then, by analyzing successful transformation cases in China, we extract data and information to explore the application and effectiveness of these strategies in practice. The results of the study show that by implementing ESGI-based transformation strategy, LMPSVs can not only make significant progress in terms of environmental protection, social responsibility, and governance structure, but also drive long-term corporate growth and competitiveness through innovation. Specifically, these results are realized in terms of increased efficiency in energy use, improved waste management, community involvement and increased stakeholder satisfaction. The study concludes by emphasizing that the SSD-based ESGI Transformation Strategic framework provides a sustainable pathway for LMPSVs. In the future, the extension of this transformation strategy will help a wider range of industry sectors to achieve sustainable development goals, and has wide application potential and far-reaching social significance.

Using assembly theory, we investigate the assembly pathways of binary strings of length N formed by joining bits present in the assembly pool and the strings that entered the pool as a result of previous joining operations. We show that the string assembly index is bounded from below by the shortest addition chain for N, and we conjecture about the form of the upper bound. We define the degree of causation for the minimum assembly index and show that for certain N it features regularities that can be used to determine a shortest addition chain. We show that a string with the smallest assembly index for N can be assembled by a binary program of length equal to this index if the length of this string is expressible as a product of Fibonacci numbers. We conjecture that there is no binary program that has a length shorter than the length of the string having the largest assembly index for N that could assemble this string. The results confirm that four Planck areas provide a minimum information capacity that corresponds to a minimum thermodynamic (Bekenstein-Hawking) entropy. Knowing that the problem of determining the assembly index is at least NP-complete, we conjecture that this problem is NP-complete, while the problem of creating the string so that it would have a predetermined largest assembly index is NP-hard. The proof of this conjecture would imply P ≠ NP, since every computable problem and every computable solution can be encoded as a finite binary string.

This comprehensive analysis aims to offer valuable insights into the safety profiles of these glucocorticoids, facilitating the identification of compounds with potentially reduced adverse effects. This study investigates the toxicity profiles of various glucocorticoids, utilizing predictions from the pKCSM Server. Among the glucocorticoids examined, Dexamethasone, Triamcinolone, Hydrocortisone hemisuccinate, Paramethasone, Fluprednisolone, and Flumethasone emerge as potentially less toxic, displaying favorable scores across diverse parameters such as Max. Tolerated Dose (human, MRT), Oral Rat Acute Toxicity (LD50), and Oral Rat Chronic Toxicity (LOAEL). Notably, Hydrocortisone hemisuccinate and Triamcinolone stand out for their high tolerability capacity based on the Max. Tolerated Dose (human, MRT), suggesting enhanced safety profiles. These findings contribute insights into selecting glucocorticoids with optimal safety and tolerability for potential therapeutic applications.Further research and clinical investigations are warranted to validate and refine these findings, ensuring the responsible and effective use of glucocorticoids in medical practice.

This paper presents the Aether Physics Model (APM), a novel theoretical framework that offers a unified description of the fundamental forces and weak interaction. The APM proposes a quantum Aether composed of discrete units, recognizes two distinct charge manifestations, and treats all charges as distributed. Using dimensional analysis and simple equations, the APM derives force equations that unify gravity, electromagnetism, and the strong forces and the weak interaction. The model makes specific predictions about force strengths, maximum mass density, and fine structure constants for protons and neutrons. These predictions align with existing observations and offer new avenues for experimental testing. The APM addresses the limitations of the Standard Model while providing a more intuitive explanation for phenomena like the quantum Hall effect and Casimir effect. If validated, the APM could revolutionize our understanding of space, matter, and fundamental forces.

Maxwell's energy-momentum tensor cannot be used as is in Einstein's equation for general relativity in the presence of sources. In this case, its four-divergence is non-zero, whereas the tensors in the equation must have this characteristic. However, the sun emits streams of ionized particles, gases are present within galaxies, material transfers in the form of ionized plasma can occur in binary star systems, and so on. These charges cannot be neglected in the study of space-time in these regions. Here, we present a modified version of this tensor that eliminates this flaw, provided that the potentials satisfy the Lorenz gauge. The distribution of sources will also be analyzed in light of fluid mechanics, allowing us to account for its influence in terms of generated pressure.

The recent mountain pine beetle outbreaks have caused widespread mortality among lodgepole pine trees in western North America, resulting in a reduced population of surviving trees. While previous studies have focused on the cascading impacts of these outbreaks on the physiology and growth of the surviving trees, there remains a need for a comprehensive study into the interactions among various physiological traits and the growth in post-outbreak stands. Specifically, the relationship between chemical (primarily terpenes) and anatomical (mainly resin ducts) defences, as well as the allocation of non-structural carbohydrates (NSCs) to support these defence modalities, is poorly understood. To address these gaps, we conducted a field survey of surviving lodgepole pine trees in post-mountain pine beetle outbreak stands in western Canada. Our retrospective analysis targeted to determine correlations between the post-outbreak concentrations of monoterpenes, diterpenes, and NSCs in the phloem, and the historical resin duct characteristics and growth traits before and after the outbreak. We detected strong correlations between post-outbreak concentrations of monoterpenes and historical resin duct characteristics, suggesting a possible link between these two defence modalities. Additionally, we found a positive relationship between NSCs and the total concentrations of monoterpenes and diterpenes, suggesting that NSCs likely influence the production of these terpenes in lodgepole pine. Furthermore, historical tree growth patterns showed strong positive correlations with many individual monoterpenes and diterpenes. Interestingly, while surviving trees had enhanced anatomical defences after the outbreak, their growth patterns did not vary before and after the outbreak conditions. The complexity of these relationships emphasizes the dynamics of post-outbreak stand dynamics and resource allocation in lodgepole pine forests, highlighting the need for further research. These findings contribute to a broader understanding of conifer defenses and their coordinated responses to forest insect outbreaks, with implications for forest management and conservation strategies.

HIGHLIGHTS Mach's principle is linked with inertia and the mass around. Nowadays, physicists believe its effect is infinitesimal, so Mach's principle is simply ignored. This paper looks at the consequence of applying Mach's principle. It could replace dark matter. ABSTRACT After seeing the history of Mach's principle, we will work out a new model of galaxies inspired by Mach’s principle, which is more satisfactory than current dark matter models. This new model is just maths and needs an explanation. One possible explanation is obviously that Mach's principle is at work in a galaxy.

Substance use disorder (SUD) is a global health crisis that is becoming increasingly prevalent among younger generations and impacting the overall health of millions of people worldwide. Traditional treatment methods, including cognitive-behavioral therapies and medicinal-assisted treatments, often face limitations such as inconsistent efficacies and varying degrees of accessibility. Thus, there is a clear need for a new and innovative treatment strategy. Music therapy has recently gained attention through research as a promising non-pharmacological supplement to strengthen conventional SUD treatment methods. This review explores the potential benefits of music therapy in reducing substance cravings, alleviating depressive symptoms, and improving emotional expression among patients in the rehabilitation phase of SUD recovery. We review evidence from various studies and clinical trials to evaluate the efficacy of music therapy in improving standard treatment outcomes, with the results indicating that music therapy could be an effective complement to traditional treatment methods while noting that its efficacy could vary depending on the individual and type of intervention provided. The benefits of music therapy include accessibility of treatment for diverse patient groups and the potential for highly individualized care, although more research is needed to compare the efficacies of specific music treatment types for certain conditions and patient populations. Further research is also needed to validate the neurobiological mechanisms of music therapy, as well as increased communication between music therapists worldwide to enforce consistency in treatment methods and share learnings in this growing field.

This communication is to summarize our single center experience on applications of anti-IL-1 biologic response modifiers on autoimmune and autoinflammatory conditions in children. We present our rationale for off-label utilization of Anakinra and discuss evolving treatment paradigms that warrants further research and validation. Based on our observations and successful treatment outcomes, we advocate future multilateral efforts to liberate access to anti-IL-1 medications to control inordinate and detrimental inflammation in children.

This paper systematically discusses the application of reinforcement learning in automatic driving system. Reinforcement learning frameworks show significant advantages in optimizing decision making, predictive perception, path planning, and controller design, exceeding the limitations of traditional supervised learning methods. The paper highlights the critical role of components such as scene understanding, positioning, and map making in autonomous driving systems, which provide reliable environmental awareness through deep learning and sensor fusion technologies to support intelligent decision-making in complex urban environments. In addition, the paper discusses innovative approaches to safety reinforcement learning to reduce risk in autonomous driving and ensure that systems adhere strictly to safety constraints while maximizing expected rewards. These findings provide an important theoretical and practical basis for further improving algorithm robustness, managing multi-agent interactions, and integrating ethical considerations in the future.

The distribution of zakat, which is still carried out directly through mosques or zakat amil bodies, can hinder the payment process due to inaccurate calculations and difficulties in finding zakat amil institutions. In the digital era, information technology is very important in the management of zakat, alms, and infaq. Therefore, a zakat information management system was created. The e-Zakat program, developed using the waterfall method, plays an important role in five main aspects of zakat management: muzaki and mustahiq data collection, zakat collection, zakat distribution, mustahiq development, and reporting. The use of information technology in this program is expected to increase efficiency, accountability, transparency, as well as the reach of zakat and muzaki involvement.

The deaf-mute and individuals with hearing disabilities who communicate through sign language should not be overlooked or excluded. It is a social responsibility to strive for an inclusive world without inequality. Advances in technology offer various solutions to address the communication gap, one of which is the use of computer-based sign language recognition systems. This project proposes a Malaysian Sign Language recognition model utilizing MobileNetV1, a convolutional neural network-based algorithm, to interpret sign language in real-time settings through a website application using Streamlit. The outcome of the model test shows that it has achieved a recognition rate of 96 percent. Although the proposed model has demonstrated the ability to recognize sign language, it is suggested that further improvement can be achieved by enhancing the quality and diversity of the dataset. The implementation of a more comprehensive dataset would lead to improved performance and increased accuracy of the model. This project underscores the importance of considering and accommodating the needs of the deaf-mute and individuals with hearing disabilities and highlights the potential of technology to bridge communication barriers and achieve a more inclusive society.

In this study, we utilized the UniProt database to extract breast cancer genes Total 2658 genes in Homo sapiens are obtain. Then 166 genes are taken and after delating duplicates 158 genes were remains. These genes were then analyzed for their biological pathways using Gene Ontology enrichment analysis through the Genecodis platform, resulting in the identification of 12 unique genes involved in disease pathways. Protein-protein interaction (PPI) network analysis was conducted using the STRING database and Cytoscape software, identifying ANLN,TREM1,LZTFL1,OXSM,PLA2G3,TAS2R13,ABCB10,ECT2,TRPC,BCAS3,PLA2G3,ZDHHC7 and ERBB2 as potential gene targets for breast cancer.

We solve the problem of Apollonius by applying the Lorentz transformation within the frame- work of special relativity to go to a frame of simultaneity, where the solutions are the circum- center. We transform back to get the solutions in a general frame. We show that the solutions in a general frame are the foci of the ellipse going through the centers of the given 3 circles.

Objective To investigate nailfold capillary parameters in community-dwelling individuals aged over 60 years who have hypertension and do not exercise regularly. Furthermore, the study examined the correlations between capillary function and other health-related indicators. Design A single- center pilot trial Setting Faculty of Health, Tsukuba University of Technology, Japan Participants hypertensive community-dwelling elderly people Intervention Microcirculation was observed before and 1 min after arm-curl exercise by means microscopy of capillary with non-exercised limb. Additionally, we examined other health-related indicators. Outcome measures: Acute effects of reperfusion on nailfold density, flow, and diameters. Secondary outcomes included the correlations between microvascular parameters and other health-related indicators. We hypothesized that brief exercise could enhance microcirculation reperfusion and correlate with other health-related parameters.　 Results: There were 20 participants with a mean (SD) age of 67.1 (5.8) years. The capillary flow rate changed from 2.3±6.7 to 2.7±0.2 log µm/s (P

In genetic conflicts, driver and killer elements achieve biased survival, replication, or transmission over sensitive and targeted elements through a wide range of molecular mechanisms, including mimicry. Driving mechanisms manifest at all organismal levels, from the biased propagation of individual genes, as demonstrated by transposable elements, to the biased transmission of genomes, as illustrated by viruses, to the biased transmission of cell lineages, as in cancer. Targeted genomes are vulnerable to molecular mimicry through the conserved motifs they use for their own signaling and regulation. Mimicking these motifs enables a selfish element to control core target processes, and can occur at the sequence, structure, or functional level. Molecular mimicry was first appreciated as an important phenomenon more than twenty years ago. Modern genomics technologies, databases, and machine learning approaches offer tremendous potential to study the distribution of molecular mimicry across genetic conflicts in nature. Here, we explore the theoretical expectations for molecular mimicry between conflicting genomes, the trends in molecular mimicry mechanisms across known genetic conflicts, and outline how new examples can be gleaned from population genomic datasets. We discuss how mimics involving short sequence-based motifs or gene duplications can evolve convergently from new mutations. Whereas, processes that involve divergent domains or fully-folded structures occur among genomes by horizontal gene transfer. These trends are largely based on a small number of organisms and should be reevaluated in a general, phylogenetically independent framework. Currently, publicly available databases can be mined for genotypes driving non-mendelian inheritance patterns, epistatic interactions, and convergent protein structures. A subset of these conflicting elements may be molecular mimics. We propose approaches for detecting genetic conflict and molecular mimicry from these datasets.

Understanding clinical significance of variants of unknown significance (VUS) reported in next-generation sequencing (NGS) has become essential in cancer treatment. Our study examined six widely used in silico tools: PolyPhen-2, Align-GVGD, MutationTaster2, CADD, REVEL, and Chat-GPT. We utilized a dataset of gene variants known to potentially affect immune therapy. No single tool could comprehensively determine mutation variant pathogenicity. MutationTaster2021 showed the highest overall accuracy and MCC among the tools. Notably, REVEL and Chat-GPT exhibited 100% specificity, suggesting their proficiency in accurately identifying pathogenic variants and minimizing false positives. In contrast, CADD displayed optimal sensitivity, making it suitable for effectively ruling out benign variants.

Based on recent progress in cosmological thermodynamics, we present a new thermody- namic formulation of the Friedmann equations in the new Haug-Tatum cosmology model, as well as for other similar models put forward in the literature from different solutions to Einstein’s field equation. Since the CMB temperature has been measured much more accu- rately than the Hubble constant, we demonstrate that our thermodynamic formulation of the Friedmann equation dramatically narrows the acceptable range for the critical density of the universe. Furthermore, we show how our thermodynamic formulation leads to a more precise cosmological constant estimate.

In many countries, increasing concern for animal welfare is driving retailer commitments and government legislation that aim to improve the lives of farmed fish. One aspect of fish welfare involves stunning fish prior to slaughter. The feasibility of stunning depends on the species of fish and physical farm characteristics. In this article, we provide an overview of stunning before slaughter in European sea bass and sea bream aquaculture, one of the largest finfish farming industries in the developed world that does not yet stun most of its production. Sea bass and sea bream stunning necessitates the use of electrical stunning equipment aboard harvest vessels, often a significant distance from the shoreline; this presents an interesting engineering and policy challenge. Together, Türkiye, Greece, Spain, and Italy produced over 400,000 t of sea bass and sea bream in 2020. In Türkiye and Greece, farms are numerous and located very close to the shoreline. In Spain and Italy, farms are few and located far from the shoreline. The highest average production is found in farms from Türkiye (1,000 t) and Spain (1,300 t, and lower average production is found in Greece (300 t) and Italy (350 t). Producer progress towards the installation of electrical stunning appears comparatively well-developed for Türkiye, Spain, and Greece, though we emphasise that producers and other stakeholders require continued support to realise this opportunity. Producers in Italy appear slower to make progress on this aspect of animal welfare and may require additional support.

The Casimir effect can either work over very short distances or at very low temperatures. At the cosmic scale, we naturally deal with long distances, but at the same time, we encounter extraordinarily low temperatures, namely the CMB (Cosmic Microwave Background) tem- perature. Recently, there has been increased interest in the Casimir effect and its implications in cosmology. Here, we briefly demonstrate that the luminosity and radiation pressure of the CMB is mathematical identical to a theoretical Casimir effect within the Hubble sphere, or actually our solution seems to be valid for any Schwarzschild black hole. The Casimir effect forevery Schwarzschild black hole is identical. This also include the Hubble sphere if we treat it as a Schwarzschild black hole that is related to increased interest in black hole cosmology.

Caused by age or trauma, collapsed connective tissue can cause nerve entrapment and damage within the tarsal tunnel. Tarsal tunnel syndrome is relatively underdiagnosed. This study presents an intervention targeting damaged tissues surrounding the nerves and replacing the structural cushioning with a Wharton’s jelly tissue allograft. The eight selected patients from four clinical sites had tarsal tunnel-related defects. Patient outcomes were tracked on a 90-day calendar utilizing the Numeric Pain Rating Scale (NPRS) and the Western Ontario and McMaster University Arthritis Index (WOMAC). All patients had failed standard care practices for at least six weeks. Each patient received a Wharton’s jelly tissue allograft to sites around the affected tarsal tunnel. No patients experienced adverse reactions. Percent change calculated from the initial application to the 90-day follow-up showed an improvement of 59.43% in NPRS and a 37.58% improvement in WOMAC. This study provides evidence that WJ allograft applications are safe, minimally invasive, and efficacious for patients who have failed standard care treatments for tissue defects associated with Tarsal Tunnel syndrome. Limitations include the small cohort size and nonblinded nature. The results of this study warrant further research to confirm the efficacy, optimal dose, protocol, and durability of Wharton's jelly.

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

The dermatological manifestations of Long Covid (LC) have languished in the shadows of chronic fatigue and brain fog. Yet they are all linked by gut dysbiosis and the cytokine triad of TNF-α, IL-1β, and IL-6. The gut microbiome common not only to LC, psoriasis, AA, and vitiligo but also to neurodegenerative disease has been recently described. This gut microbiome induces an altered tryptophan metabolism linked to autoimmune disease. SARS CoV2 invades enterochromaffin cells rich in ACE2 receptors and curtails absorption of the essential amino acid tryptophan and subsequent synthesis of serotonin and melatonin. This review suggests that an etiologic prebiotic (d-mannose)/probiotic (lactobacilli, bifidobacteria)/postbiotic (butyrate) approach to autoimmune skin disease that improves intestinal barrier integrity and that suppresses the triad of TNF-α, IL-6, and IL-1β may enhance or even eliminate the traditional immunotherapy of targeted monoclonal antibodies, Janus kinase inhibitors, and steroids. Health benefits of this approach extend well beyond suppression of autoimmune skin disease.

Upon the outbreak of the COVID-19, countries worldwide developed policies with the goal to introduce restrictive measures geared towards mitigating the spread of the virus. Although these health initiatives were put in place through top-down public directives, they relied heavily on the cooperation of citizens who had to be willing to carry them out. As a consequence, we could observe differences in compliance with governmental restrictions within and between countries. Increasing policy stringency was the method adopted overall to promote people’s compliance. This article used survey data from 10308 participants in Brazil to examine whether there was a positive correlation between policy stringency and compliance with restrictive measures to reduce the spread of COVID-19. The results revealed, however, that there was no direct influence of policy stringency on people’s health behaviors. Accordingly, I hypothesized that individual differences in personality traits were one of the drivers behind policy compliance, as they would support self-interests such as the duty to support authorities as well as the perceived health risks of certain behaviors and ultimately motivate compliance. The findings’ implications for both compliance research and for authorities wanting to nurture voluntary compliance with public health orders are discussed and suggestions are provided.

This work introduces a novel facial image capture system that utilizes computer vision technology and artificial intelligence for real-time detecting, tracking, and capturing of human faces. The objective of this study is to address the challenges posed by poor-quality facial images in biometric authentication, especially in passport photo acquisition and recognition. By combining face-aware capture technology with AES encryption for secure image storage, we present a completely open-source hardware solution that consists of a Jetson processor, a 16MP autofocus RGB camera, a custom enclosure, and a touch sensor LCD for user interaction. Pilot data collection demonstrates the system's ability to capture high-quality images, achieving a 98.98% accuracy in storing images of acceptable quality. The integration of AES encryption ensures data security, making the proposed system suitable for real-time applications in other domains beyond identity verification in passport applications, such as security systems, video conferencing, etc

Albert Einstein, shortly after publishing his General Relativity Theory (GRT) applied it to the entire universe in a paper of 1917. Einstein assumed that such a universe was static in time and possessed a uniform distribution of matter on the largest scales, obtaining a static and finite universe of spherical spatial curvature of radius R. Although this model was refuted by Edwin Hubble's discovery of the expansion of the universe, it is still often studied as a theoretical framework for some problems, such as the twin paradox. This paper will show that Einstein's static universe model leads to a privileged frame of reference, so that any observer moving at the speed v will be able to measure speeds of light other than c depending on the direction of beam transmission. The above will refute the validity of the application to this closed and static universe of the Special Relativity Theory (SRT) proposed by Einstein in 1905 in his article . In addition, a new synchronization convention and new coordinate transformations will be deduced, the "generic" transformations, which will replace those of Lorentz. Finally, the twin paradox in this universe will be solved by making use of them.

Exosomes, a subset of extracellular vesicles, have emerged as pivotal mediators of intercellular communication, revolutionizing our comprehension of cellular interactions. Their diverse cargo, comprising proteins, nucleic acids, and bioactive molecules, plays a crucial role in various physiological and pathological processes. In the intricate landscape of adult neurogenesis, exosomes wield significant influence, actively participating in the generation of new neurons within the adult mammalian brain. Originating predominantly from non-niche cells, these vesicles contribute substantially to the regulation of neuroinflammation, thereby impacting neurogenesis and offering therapeutic promise for neurological disorders. In the specific context of Parkinson's disease (PD), exosomes assume a multifaceted role, prominently implicated in the propagation of α-synuclein oligomers and the orchestration of neuroinflammation. Stem cell-derived exosomes present themselves as promising therapeutic agents, capable of reshaping the microenvironment, repairing neuronal damage, and mitigating apoptosis. The ongoing exploration of exosomes continues to unravel intricate biological processes, paving the way for innovative medical interventions and treatments, particularly within the challenging landscape of neurodegenerative diseases such as PD. The comprehensive understanding of exosomal functions holds the potential to advance our therapeutic approaches, offering new avenues for intervention and treatment strategies in the realm of neurodegenerative disorders.

Among the number of hurdles that the vaccines based in modRNA platforms have been facing, there is now another critical one: immune imprinting which decreases even more the effectiveness of the modRNA Covid vaccines.

The initiation of micro-cracks in concrete is primarily due to its brittleness, which causes severe impacts on the overall behavior of the concrete. However, incorporating fibers in concrete could significantly improve its mechanical properties. The current study demonstrates the influence of two types of fibers on the mechanical properties of concrete. Nylon Fiber Threads (NFT) and Nylon Fiber Strands (NFS) at a fiber content of 2.5 kg/m³ were incorporated in the concrete mix. Concrete with a compressive strength of 30 MPa and a water-cement ratio of 0.4 was produced to investigate the fresh and mechanical properties of conventional and nylon-fiber reinforced concrete (NFRC). The concrete prims and cylinders were tested for compressive, splitting, and flexural strength using ASTM standards (ASTM C-39, C-496, and D-790, respectively). It was found that NFT-reinforced samples achieved higher mechanical properties of concrete (13.05%) compared to NFS-reinforced samples. Test results showed that incorporation of nylon fibers (NF) in concrete decreased the compressive strength of cylindrical and cubical specimens by 0–10% and 5–15%, respectively. However, it increased the splitting tensile and flexural strengths by 0-15%, and 5-15% respectively. Furthermore, the incorporation of NF in concrete significantly improved the ductility and delayed the crack initiation in concrete.

The Power Theory of Exchange and Money (PTEM) refines the economic neoclassical two-equation model of exchange. PTEM replaces the statistical average condition of exchange, which represents the equality of supply and demand for all transactions in the market, with a more specific equation of the equality of the generalized power of the parties in relation to their objects of exchange. The purpose of the article is to show the application of PTEM for the study of various social phenomena. Along with a brief summary of the main statements of this theory, its integration into the existing corpus of theories and ideas in economics and sociology is considered. The conclusions of power theory are then applied to consider the following topics: the objective motivation of exchange by the desire to increase power, the power nature of market equilibrium, the power nature of money, the accumulation of power through profit, the creation of money as a power act, the role of the state in the creation of money, the measure of power as a possible tool for social and political research.

In the domain of financial trading, where accuracy and swift response to data discrep- ancies are paramount for maintaining clienttrust, our research paper delves into the development of a real-time alert system utilizing SpringBoot, a framework renowned for its robustness and adaptability in enterprise applications. We embark on a comprehensive journey, beginning with addressing thefoundational challenges inherent in monitoring trade inquiries - the necessity for real-time processing, unwavering data accuracy,and ensuring unwavering system reliability. The study then methodically transitions to the architectural design, where we explore howSpringBoot’s capabilities are optimally utilized to meet these challenges. Following this, we delve into the im- -plementationphase, detailing the strategic selection of compatible technologies and methods for effective data management and discrepancydetection. The paper culminates in a thorough evalu- ation of the system, where we assess performance across various operationalscenarios, scrutinize the accuracy of the discrepancy detection mechanisms, and integrate real-world feedback from users in thetrading sector. This holistic approach not only highlights the versatility and efficiency of SpringBoot in creating reliable alert systems in the high-stakes world of financial trading but also serves as a detailed guide for similar endeavors in this field

As digitalization expands across all sectors, the economic toll of software defects on the U.S. economy reaches up to $2.41 trillion annually. High-profile incidents like the Boeing 787-Max 8 crash have shown the devastating potential of these defects, highlighting the critical importance of software testing within quality assurance frameworks. However, due to its complexity and resource intensity, the exhaustive nature of comprehensive testing often surpasses budget constraints. This research utilizes a machine learning (ML) model to enhance software testing decisions by pinpointing areas most susceptible to defects and optimizing scarce resource allocation. Previous studies have shown promising results using cost-sensitive training to refine ML models, improving predictive accuracy by reducing false negatives through addressing class imbalances in defect prediction datasets. This approach facilitates more targeted and effective testing efforts. Nevertheless, the generalizability of these models across different projects (cross-project) and programming languages (cross-language) remained untested. This study validates the model's applicability across diverse development environments by integrating various datasets from distinct projects into a unified, using a more interpretable ML approach. The results demonstrate that ML can support software testing decisions, enabling teams to identify up to seven times more defective modules with the same testing effort as a benchmark.

Software is playing the most important role in recent vehicle innovation, and consequently the amount of software has been rapidly growing last decades. Safety-critical nature of ships, one sort of vehicles, makes Software Quality Assurance (SQA) has gotten to be a fundamental prerequisite. Just-In-Time Software Defect Prediction (JIT-SDP) aims to conduct software defect prediction (SDP) on commit-level code changes to achieve effective SQA resource allocation. The first case study of SDP in maritime domain reported feasible prediction performance. However, we still consider that the prediction model has still rooms for improvement since the parameters of the model are not optimized yet. Harmony Search (HS) is a widely used music-inspired meta-heuristic optimization algorithm. In this article, we demonstrated that JIT-SDP can produce the better performance of prediction by applying HS-based parameter optimization with balanced fitness value. Using two real-world datasets from the maritime software project, we obtained an optimized model that meets the performance criterion beyond baseline of previous case study throughout various defect to non-defect class imbalance ratio of datasets. Experiments with open source software also showed better recall for all datasets despite we considered balance as performance index. HS-based parameter optimized JIT-SDP can be applied to the maritime domain software with high class imbalance ratio. Finally, we expect that our research can be extended to improve performance of JIT-SDP not only in maritime domain software but also in open source software.

This review paper delves into the multifaceted aspects of pineapple cultivation, focusing on morphology studies, harvesting challenges, and mechanization efforts. Pineapple, renowned for its commercial value and unique flavor, holds a prominent position in global agriculture, with India alone producing 19.64 lakh tonnes annually. However, the hilly terrains of regions like northeastern India pose significant hurdles to traditional harvesting methods, necessitating innovative solutions. Understanding the morphology of pineapple plants is crucial for optimizing cultivation practices and designing efficient harvesting equipment. Insights from various studies shed light on leaf structure, fruit maturity, and growth patterns, providing valuable information for engineering tailored solutions. Mechanization endeavors, including semi-mechanized harvesting machines and semi-manual harvesters, demonstrate promising results in improving efficiency and reducing labor-intensive practices. Despite challenges such as increased crop losses and operational costs, continued research and development in mechanization hold the key to sustainable pineapple cultivation. This paper underscores the importance of integrating morphology studies and mechanization efforts to address the unique challenges faced by pineapple farmers, particularly in hilly terrains, and highlights the need for ongoing innovation to ensure the long-term sustainability of pineapple cultivation.

The quality of wine depends upon the quality of the grapes, which, in turn, are affected by different viticulture aspects and the climate during the grape-growing season. Obtaining wine professionals’ judgments of the intrinsic qualities of selected wine products is a time-consuming task. It is also expensive. Instead of waiting for the wine to be produced, it is better to have an idea of the quality before harvesting, so that wine growers and wine manufacturers can use high-quality grapes. The main aim of the present study was to investigate the use of machine learning aspects in predicting wine quality and to develop a pipeline which represents the major steps from vineyards to wine quality indices. This pipeline outputs the predicted yield, values for basic parameters of grape juice composition, values for basic parameters of the wine composition, and quality. We also found that the yield could be predicted because of input data related to the characteristics of the vineyards. Finally, through the creation of a web-based application, we have investigated the balance berry yield and wine quality. Using these tools further developed, vineyard owners should be able to predict the quality of the wine they intend to produce from their vineyards before the grapes are even harvested.

Financial market data analysts are increasingly turning towards machine learning techniques for data analysis and making decisions. This paper explores how knowledge graphs, essentially a network of real entities and their relationships, can be used to improve collaborative financial market data analysis and make it more efficient and user friendly. This paper reviews the current state of financial market data analysis, the challenges in deploying machine learning models in industrial environments, and the concepts such as ML-Ops and knowledge graphs. A software architecture comprising various roles, layers and services such as Data Engineers, Data Analysts, public users, APIs, workflows, analytics libraries, UX layer, Business Layer, etc. is introduced and described in detail. Then, it discusses how knowledge graphs can be used to enhance collaborative financial market data analysis. Finally, a case study is presented to demonstrate the usage of the whole system, within the context of financial market data analytics of equities.

CAD-CAE software companies have introduced numerous tools aimed at facilitating topology optimization through Finite Element Simulation, thereby enhancing accessibility for designers via user-friendly interfaces. However, the imposition of intricate constraint conditions or additional restrictions during calculations may introduce instability into the resultant outcomes. In this paper, an algorithm for updating the design variables called Adaptive Variable Design is proposed to keep the final design space volume of the optimized part always under the target value while giving the main algorithm multiple chances to update the optimization parameters and search for a valid design. This algorithm aims to producing results that are more conducive to manufacturability and potentially more straightforward in interpretation. A comparison between several commercial software and the proposed algorithm, implemented in MATLAB, is carried out to prove the robustness of the latter. By simulating identical parts under similar conditions, we seek to generate comparable results and underscore the advantages stemming from the adoption and comprehension of the proposed topology optimization methodology. Our findings reveal that the integrated enhancements within MATLAB pertaining to the topology optimization process yield favourable outcomes with respect to discretization, and the manufacturability of resultant geometries. Furthermore, we assert that the methodology evaluated within MATLAB holds promise for potential integration into commercial packages, thereby enhancing the efficiency of topology optimization processes.

Hybrid electric vehicles (HEVs) are perceived as a first step towards the future of sustainable transport. Of course, battery electric vehicles (EVs) are currently ideal for what is wanted to stand for transport in the future. The lack of infrastructure for these vehicles makes many potential users choose hybrid vehicles in these conditions. This paper analyzes the influence of the engine and electric motor of hybrid vehicles on their performances. Three engines with slightly different power and close characteristics were considered for configured models. Also, two electric motors with very different power but having a very close maximum continuous torque have been used in models. One is an induction motor, and the other is a permanent magnet synchronous motor. ADVISOR software has been used for vehicles configuration and simulation. Series and parallel hybrid vehicles have been considered. The main dynamic performances and the fuel consumption in two driving cycles were compared for the configured models. Three conventional models with the same engines used in HEVs have also been simulated for reference. The results highlight that in the case of HEVs, the choice of combination engine/electric motor is crucial in obtaining the best report between dynamic performances and low fuel consumption, and implicitly a low negative impact on the environment.

Quantum computing is tipped to lead the future of global technological progress. However, the obstacles related to quantum software development are an actual challenge to overcome. In this scenario, this work presents an implementation of the quantum search algorithm in Atos Quantum Assembly Language (AQASM) using the quantum software stack my Quantum Learning Machine (myQLM). We present the creation of a virtual quantum processor whose configurable architecture allows the analysis of induced quantum noise effects on the quantum algorithms. The codes are available throughout the manuscript so that readers can replicate them and apply the methods discussed in this article to solve their own quantum computing projects. The presented results are consistent with theoretical predictions and demonstrate that AQASM and myQLM are powerful tools for building, implementing, and simulating quantum hardware.

Grid modernization has been ushered in by rising electricity consumption, deteriorating infrastructure, and increasing reliability concerns for electric utilities. New advances, collectively referred to as the smart grid, include modern electronics, technology, telecommunications, and computing capabilities. Smart grid telecommunication frameworks provide two-way communication to support grid operations. Software-defined networking (SDN) has been proposed as a method of monitoring and controlling telecommunication networks, enabling increased smart grid visibility, control, and security. However, being connected to telecommunications infrastructure means that smart grid networks are exposed to cyber-attacks. Attackers may use unauthorized access to intercept messages, inject false data into system measurements, flood communication channels with false data packets, or target centralized controllers to cripple network control. Defense and security techniques against these threats are constantly evolving, necessitating an up-to-date, comprehensive study analyzing cyber attacks and defense methods for smart grid networks. Previous smart grid security surveys do not contain recent techniques and to our knowledge most, if not all, address only one type of attack type and/or one type of defense. This survey considers the latest security techniques, simultaneous multi-pronged cyber attacks and defensutilityes to meet the challenges of the next-generation SDN smart grid research with the goal of identifying future research needs, describing the open security challenges, and exposing both emerging threats and their potential impact on SD-SG deployment.

The high-quality development of the industrial software industry is of strategic significance to enhancing the core competitiveness of the manufacturing industry and promoting the high-quality development of China's industrial economy. By integrating the "capital- technology-environment-human" production factor theory and configuration perspective, this paper constructs a comprehensive analysis framework that drives the total factor productivity (TFP) of the industrial software industry. It uses 40 typical industrial software firms as case samples and uses fuzzy set Qualitative Comparative Analysis (fsQCA) was used to empirically explore the influencing factors and complex mechanisms that achieve high-quality development of the industrial software industry. It is found that: (1) a single industrial factor is hardly a necessary condition to drive industrial software industry; (2) there are four paths to achieve high TFP, which are summarized as "technology-human-environment" balanced driving type, "capital-human-environment" balanced driving type, "technology-capital" dual driving type, and "capital" single driving type. There are four driving mechanisms. There are also four not-high TFP configurations with asymmetric characteristics; (3) under certain conditions, the combination of capital factors, capital factors, environmental factors and human factors can drive TFP in a "all roads lead to Rome". In this process, the government's attention plays a more universal role. The study not only expands the application scenarios of fsQCA, but also provides decision guidelines for the practice of strategic emerging industrialization represented by industrial software industry.

Epidemiological surveillance and phylogenetic studies rely nowadays on processing and analysing huge volumes of data. Processing tasks consist on running and refining a series of intertwined computational tasks. And, despite of existing several web applications for data processing and interactive visualization for phylogenetic studies, integrating many different tools and algorithms, their execution is total or partially on the client side, making them unsuitable for dealing with huge volumes of data. Studies are often also not easy to reproduce. On the other hand, in recent years, data-centric workflow systems have been proposed, allowing to deal better with increasingly larger datasets. The integration of these systems within phylogenetic tools will allow to scale them as required, and will contribute also to promote studies reproducibility. We propose then the FLOWViZ middleware for facilitating the integration of a state of the art data-centric workflow system, Apache Airflow, within web applications for phylogenetic analyses. This framework abstracts contracts and a core API for defining tools and workflows, where tools are assumed to be containerized. FLOWViZ has been tested and evaluated within the PHYLOViZ web application, a tool supporting phylogenetic inference and data visualization.

Software testing is an integral yet time-consuming component of software development, and even with capable software testing resources (technology and people), it is still regarded as problematic. Test automation is widely being utilised within the software industry to provide increased testing capacity to ensure high product quality and reliability. In this paper, we are specifically addressing automated testing whereby test cases are manually written. Test automation has its benefits, drawbacks, and impacts on different stages of development. Furthermore, there is often a disconnect between non-technical and technical roles, where non-technical roles (e.g., management) predominantly strive to reduce costs and delivery time whereas technical roles are often driven by quality and completeness. Although it is widely understood that there are challenges with adopting and using automated testing, there is a lack of evidence to understand the different attitudes toward automated testing (AtAT), focusing specifically on why it is not adopted. In this paper, the authors have surveyed practitioners within the software industry from different roles to determine common trends and draw conclusions. A two-stage approach is presented, comprising a comprehensive descriptive analysis and the use of Principle Component Analysis. In total, 81 participants were provided with a series of 22 questions and their responses are compared against job role types and experience levels. In summary, 6 key findings are presented covering expertise, time, cost, tools and techniques, utilisation, organisation, and capacity

This manuscript explores the significance of continuous improvement in software development and digital product management, highlighting its role in addressing challenges and achieving superior results in today's dynamic business landscape. Drawing on Lean Thinking and Agile principles, the paper emphasizes the importance of customer focus, multidisciplinary collaboration, respect for people, inspection and adaptation, experimentation, incremental delivery, and continuous feedback in driving continuous improvement. It identifies key problems associated with the lack of continuous improvement, including quality issues, stagnation, inefficiency, lack of learning, lack of participation and collaboration, and lack of adaptability. To overcome these challenges, the paper proposes a solution that leverages agile events and interactions, such as sprint retrospectives and daily scrum meetings, and introduces a "kaizen board" as a visual tool to manage and monitor the continuous improvement process. The manuscript highlights the benefits of continuous improvement in terms of quality enhancement, efficiency gains, improved customer satisfaction, and adaptability to change. It stresses the need for fostering a culture of learning and constant adaptation, and provides insights on applying continuous improvement across different dimensions: product, process, team, and culture. The paper concludes by emphasizing the significance of commitment and active participation from all team members for achieving sustainable progress and success in digital transformation efforts.

Agricultural production needs technologies that assist the management of natural resources, for example, the collection of real-time data on soil, water, weather, crops, and biodiversity conditions. Sensor technology solutions and open-source software are appropriate for promoting more sustainable agricultural production. Among the advantages of using open-source technologies and software is its potential for extension, collaboration, customization, flexibility, maintenance cost, transparency, speed, and better security. Given the above, the objective of this research was to find, in different electronic databases, exclusively open-source software for precision agriculture, offering a systematic review, and addressing considerations and challenges. This survey considers up-to-date open-source software available in repositories such as GitHub and GitLab, to understand its characteristics and application formats.

In this paper we present AWEbox, a Python toolbox for modeling and optimal control of multi-aircraft systems for airborne wind energy (AWE). AWEbox provides an implementation of optimization-friendly multi-aircraft AWE dynamics for a wide range of system architectures and modeling options. It automatically formulates typical AWE optimal control problems based on these models, and finds a numerical solution in a reliable and efficient fashion. To obtain a high level of reliability and efficiency, the toolbox implements different homotopy methods for initial guess refinement. The first type of methods produces a feasible initial guess from an analytic initial guess based on user-provided parameters. The second type implements a warmstart procedure for parametric sweeps. We investigate the software performance in two different case studies. In the first case study we solve a single-aircraft reference problem for a large number of different initial guesses. The homotopy methods reduce the expected computation time by a factor of 1.7 and and the peak computation time by a factor of 8, compared to when no homotopy is applied. Overall, the CPU timings are competitive to timings reported in the literature. When the user initialization draws on expert a priori knowledge, homotopies do not increase expected performance, but the peak CPU time is still reduced by a factor of 5.5. In the second case study, a power curve for a dual-aircraft lift-mode AWE system is computed using the two different homotopy types for initial guess refinement. On average, the second homotopy type, which is tailored for parametric sweeps, outperforms the first type in terms of CPU time by a factor of 3. In conclusion, AWEbox provides an open-source implementation of efficient and reliable optimal control methods that both control experts and non-expert AWE developers can benefit from.

The development of quantum computers seeks software developers' attention regarding security in the era of Information Technology, software security is the primary goal for our quantitative assessment of software security in the development cycle of software. Security assessment of software is identifying the key security factors of the software. A security elective provides ex-tensive strategies and calculations to ensure product safety. The security assessment is the key factor in surveying, administering, and controlling security to further enhance the nature of safety. It should be acknowledged that assessing security early on in the development process is beneficial in identifying worms, hazards, flaws, and threats. The definition and portrayal of Quantum Computing (QC) in software security will be discussed in this study. Researchers use cryptography calculations to secure our financial institutions, medical devices, military weaponry, planes, ships, vehicles, and pilots. Here authors of this study use the Fuzzy Technique for Order Preference by Similarity to Ideal Situation (FTOPSIS) to quantitatively assess the weight/rank of the quantum enable security alternatives like (Diffie-Hellman key-exchange algorithm, Quantum key distribution algorithm, Deutsch-Jozsa Algorithm, Special Deutsch-Jozsa Algorithm, Grover’s Algorithm and Quantum key distribution algorithm in GHZ state) with security factors like (Confidentiality, Integrity, Authenti-cation, Privacy, Reliability, Maintainability, Authorization, Integrity, Possessions, and Availability). Additionally, they critically analyze and select the six alternatives of quantum-based security algorithms. The nature of safety infers the ability to execute a thing on time in this exploration study, specifically 'software security'.

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence of Free and Open-Source Models to estimate the sea-level impact can contribute to better coastal man-agement. This study aims to develop and to validate two different models to predict the sea-level rise impact supported by Google Earth Engine (GEE) – a cloud-based platform for planetary-scale environmental data analysis. The first model is a Bathtub Model based on the uncertainty of projections of the Sea-level Rise Impact Module of TerrSet - Geospatial Monitoring and Modeling System software. The validation process performed in the Rio Grande do Sul coastal plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses Bruun Rule formula implemented in GEE and is capable to determine the coastline retreat of a profile through the creation of a simple vector line from topo-bathymetric data. The model shows a very high cor-relation (0.97) with a classical Bruun Rule study performed in Aveiro coast (NW Portugal). The GEE platform seems to be an important tool for coastal management. The models developed have been openly shared, enabling the continuous improvement of the code by the scientific commu-nity.

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence 15 of Free and Open-Source Models to estimate the sea-level impact can contribute to better coastal 16 management. This study aims to develop and to validate two different models to predict the 17 sea-level rise impact supported by Google Earth Engine (GEE) – a cloud-based platform for plan-18 etary-scale environmental data analysis. The first model is a Bathtub Model based on the uncer-19 tainty of projections of the Sea-level Rise Impact Module of TerrSet - Geospatial Monitoring and 20 Modeling System software. The validation process performed in the Rio Grande do Sul coastal 21 plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses Bruun Rule for-22 mula implemented in GEE and is capable to determine the coastline retreat of a profile through the 23 creation of a simple vector line from topo-bathymetric data. The model shows a very high correla-24 tion (0.97) with a classical Bruun Rule study performed in Aveiro coast (NW Portugal). The GEE 25 platform seems to be an important tool for coastal management. The models developed have been 26 openly shared, enabling the continuous improvement of the code by the scientific community.

Security patterns encompass security-related issues in secure software system development and operations that often appear in certain contexts. Since the late 1990s about 500 security patterns have been proposed. Although the technical components are well investigated, the direction, overall picture, and barriers to implementation are not. Here, a systematic literature review of 240 papers is used to devise a taxonomy for security pattern research. Our taxonomy and the survey results should improve communications among practitioners and researchers, standardize the terminology, and increase the effectiveness of security patterns.

DevOps is an emerging practice to be followed in the Software Development life cycle. The name DevOps indicates that it’s an integration of the Development and Operation team. It is followed to integrate the various stages of the development cycle. DevOps is an extended version of the existing Agile method. DevOps aims at continuous integration, Continuous Delivery, Continuous Improvement, faster feedback and security. This paper reviews the building blocks of DevOps, challenges in adopting DevOps, Models to improve DevOps practices and Future works on DevOps

We present software on total energy calculation by quantum mechanics first principle method with a graphic user interface (GUI). Total energy calculation in this software is based on numerical analysis of time-dependent density functional (the used numerical method is finite difference time domain). QUMEC package has been equipped by common exchange-correlation energy terms with electron spin polarization calculation. With this package, users can calculate the total energy of the free particle, bulk materials, and materials with free surfaces at the atomic scale. The package is tested by several physical subjects, i.e., the surface energy of nano-LiCoO₂ and diffusion constant of lithium atoms in LiNi­_0.5Mn_1.5O₄.

This code provides computational facilities to simulate current versus time during the charging of Li-ion cells at desire constant voltage by considering multiscale physical phenomena. This code only considers a powder of active materials (at microscale or nanoscale) and a small part of electrolyte around it as a half cell. Then it is extended to a complete cell by applying correct boundary conditions. This code is very useful by modifying code parameters to understand the effect of the complex shape of active materials powder (surface area and powder size), kind of electrolyte, and the applied voltages on the charging response of Li-ion cell. As a summary, a microscale approach to the design of Li-ion cells has been provided via this code.

The crux of the paper is to present a detailed analysis of COVID-19 data which is available on global basis. This analysis is performed using some specific package of R software. It provides various insights from the data and help to understand the current status of this pandemic in India so that effective measures can be formulated by policymakers. These insights include global summary of this disease, growth rate of this pandemic and performance of SIR model for the given global data. The analysis has been presented in different tables and graphs to understand the outputs of the problem in a more detailed point of view.

A novel software engineering platform called the Dynamic Nuchwezi Architecture Platform (DNAP) is introduced, specified and its novelties explained. The unique features of this platform are explained and several new concepts and abstractions upon which its implementation, usage, and analysis are hinged also elaborately discussed. The motivations for this new approach to building especially tools used in data engineering are spelled out and the platform is contrasted against other existing technologies of a similar kind. Finally, it is shown what known limitations DNAP suffers, as well as what room for further research and improvement there is in this field.

Molecular visualisation is fundamental in the current scientific literature, textbooks and dissemination materials, forming an essential support for presenting results, reasoning on and formulating hypotheses related to molecular structure. Visual exploration has become easily accessible on a broad variety of platforms thanks to advanced software tools that render a great service to the scientific community. These tools are often developed across disciplines bridging computer science, biology and chemistry. Here we first describe a few Swiss Army knives geared towards protein visualisation for everyday use with an existing large user base, then focus on more specialised tools for peculiar needs that are not yet as broadly known. Our selection is by no means exhaustive, but reflects a diverse snapshot of scenarios that we consider informative for the reader. We end with an account of future trends and perspectives.

A university canteen is a queueing system characterised by non-stationary time of arrival with limited resources where the arrival rate is time dependent and has different pattern of arrival for different time interval. This means at certain time of the day, the arrival rate is much higher than other time and for a university canteen, the arrival rate of customer during the lunchtime is much higher and the food (resources) is limited. Non-stationary time dependent queueing system is not easily modelled mathematically hence such queueing systems are modelled using simulation tools such as ARENA. In order to model a non-stationary time dependent queueing system with limited resources and solve queueing problems using ARENA, researchers have to depend on their knowledge and experience in identifying the appropriate and relevant parameters for the system and make modifications to these parameters of the system to solve queueing problems by means of trial and error. Hence, this research work explores the potentials of applying a systematic problem solving tool, TRIZ to help users to make better decisions in deriving solutions to improve a non-stationary time dependent queueing system with limited resources. A case study was carried out to minimize the waiting time of the customers at the cafeteria of the Faculty of Engineering, Universiti Putra Malaysia (UPM), which has queueing problems for years during lunchtime. TRIZ was applied in this case study and the results showed that TRIZ can assist researchers to derive a solution model that lead to shorter waiting time without incurring additional cost and resources.

Context: Code smells are associated with poor design and programming style that often degrades code quality and hampers code comprehensibility and maintainability. Goal: Identify reports from the literature that provide evidence of the influence of code smells on the occurrence of software bugs. Method: We conducted a Systematic Literature Review (SLR) to reach the~stated goal. Results: The SLR includes selected studies from July 2007 to September 2017 which analyzed the source code for open source and proprietary projects, as well, as several code smells and anti-patterns. The results of this SLR show that 24 code smells are more influential in the occurrence of bugs according to 16 studies. In contrast, three studies reported that at least 6 code smells are less influential in such occurrences. Evidence from the selected studies also point out tools, techniques and procedures applied to analyze the influence. Conclusion: To the best of our knowledge, this is the first SLR to target this goal. This study provides an up-to-date and structured understanding of the influence of code smells on the occurrence of software bugs based on findings systematically collected from a list of relevant references in the latest decade.

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

This article continues research efforts on performance and energy efficiency of intelligent algorithms and its role for green and sustainable computing. The introduction focusses on Bremermann's Limit and its effect on extensive approach for improvement of computers performance. The article aims to identify role of Intelligent Algorithms energy efficiency, how it differs from general software energy efficiency. An improved empirical investigation on heuristic methods for search and optimisation illustrates algorithms' energy efficiency. Experimental results and consideration of further work conclude the article.

This paper discusses the application and advantages of artificial intelligence technology in digital media interactive product design. Firstly, the development background of artificial intelligence technology and its promoting effect on design innovation is introduced, and the application of new technologies, such as generative adversarial networks in art creation and design personalization, is definitely analyzed. It then explores in detail the ability of AI to break traditional constraints in interactive design, innovate design, and optimize user experience, especially in digital media. Finally, the case study of Microsoft Cortana shows the method of classifying and processing user queries by machine learning system and its experimental results. The research of this paper provides the theoretical basis and empirical support for the application of artificial intelligence technology in the future interactive design of digital media and has important academic and practical significance.

The energy transition towards Net Zero Emission by 2060 hinges on the renewable energy power plants in Indonesia. Good practices in several countries suggest a peer-to-peer (P2P) energy trading system using blockchain technology, supported by renewable energy (solar panels), an innovation to provide equal access to sustainable electricity while reducing the impact of climate change. The P2P energy trading concept has a higher social potential than the conventional electricity buying and selling approach, such as that of PLN (the grid management concept), but does not have a sharing element. This model implements a solar-powered mini-grid system and produces a Smart Contract that facilitates electricity network users to buy, sell, and trade electricity in rural areas via smartphones. This study aims to analyze the stakeholders' perceptions of the peer-to-peer (P2P) energy trading model using blockchain technology in Gumelar District, Banyumas Regency, Central Java Province, Indonesia. The stakeholders include households, the Ministry of Energy and Mineral Resources (ESDM), the State Electricity Company (PLN), the Institute for Essential Services Reform (IESR), Awina Company, and IPB University. The strength factors of this model are the freedom to generate and sell electricity (0.1) and transparency in electricity usage and sales by disclosing the amount, duration, and price of electricity (0.1). Regarding weakness factors, this model is primarily suitable for the lower middle class residing in rural or remote areas (0.02). Additionally, the intermittent electricity production to meet the substantial demand is a weakness (0.03). The opportunity factor for the model is the increase in public awareness and cooperation in using environmentally friendly, pollution-free electricity (0.12). Furthermore, there is an opportunity for additional income (0.12) and new business investment (0.12). Regarding threat factors, the model faces significant threats from the implementation aspect involving multiple parties (0.04). There is a potential threat in the form of losses for state utilities such as PLN or individuals who have already established electricity infrastructure if this program is implemented on a large scale (0.04). This model is located at the coordinate point (-0.40; 0.31), placing it in quadrant II. This indicates that the model should apply the Stability Strategy and focus on the Selective Maintenance Strategy for improvement. The stability strategy goal is to maintain the current situation by leveraging opportunities and addressing weaknesses. For effectiveness and efficiency, the strategy emphasizes fostering collaboration with PLN, which holds the legal authority as the sole utility company in Indonesia with an extensive electricity network, as well as advocating for regulations that support the independent purchase and sale of solar-powered electricity, particularly in areas without PLN network coverage.

This paper presents a mathematically rigorous investigation into the incompleteness of Einstein-de Sitter spacetime. The incompleteness of the spacetime is proved using results regarding isometries between connected Semi-Riemannian manifolds following the computation of the Einstein tensor of the Einstein-de Sitter spacetime. Various properties of the Einstein-de Sitter spacetime apart from its incompleteness is also discussed based on its incompleteness with the help of various results of Semi-Riemannian manifolds. Also, few properties of spacetime which cannot be concluded from the incompleteness of the spacetime is also discussed.

Preoperative assessment is crucial in surgical care, traditionally relying on physical examinations and paper-based questionnaires. The emergence of digital tools like Pathpoint® ePOA represents a significant advancement, aiming to enhance the efficiency, accuracy, and standardisation of preoperative evaluations. Pathpoint® ePOA is a cloud-based platform designed to integrate seamlessly with Electronic Health Records (EHRs), providing a comprehensive, data-driven approach to preoperative assessments. It supports improved risk stratification, optimised resource allocation, and enhanced patient engagement through features such as customisable templates and real-time data integration.Current research highlights that digital preoperative tools like ePOA offer notable advantages, including increased clinical capacity, better complexity stratification, and improved patient follow-up. Despite these benefits, gaps remain in comparative studies, detailed clinical outcomes, and patient engagement strategies, indicating areas for further exploration. Pathpoint® ePOA adheres to rigorous safety standards and risk management protocols, ensuring reliability and data security while contributing to improved surgical outcomes. The benefit-risk ratio for ePOA is favourable, underscoring its potential to advance preoperative care by optimising clinical workflows and enhancing patient safety.

This study discusses the application of deep learning technology in network intrusion detection systems (IDS) and focuses on a new model named CNN-Focal. First, through the review of traditional IDS technology, it analyzes its limitations in dealing with complex network traffic. Then, the design principle of the CNN-Focal model is described in detail, which uses threshold convolution and SoftMax multi-class classification technology to effectively improve abnormal traffic detection's accuracy and efficiency. The experimental results show that CNN-Focal performs well on the open data set, demonstrating the potential and advantages of its application in the natural network environment and providing a new perspective and method for further research of deep learning in the field of network security in the future.

The theory says that number interacts with spaces based on the meaning of mathematics, in this way it is possible to obtain numerical knowledge. The theory shows the possibility of using spaces related to each other to explain the reasons why two even numbers form a pair, two odd numbers form a pair, an even number and an odd number form an odd number, three odd numbers form an odd number . three even numbers make an odd number. an even, an odd and two evens make an odd, two odd and an even make a pair. Furthermore, it is possible to extend the logic to larger numbers, but applying rules to make it work.

The Diaphorina citri Kuwayama (D. citri) is one of the major pests in the citrus industry, which spreads Citrus Huanglongbing disease. It has developed resistance to chemical insecticides. Therefore, searching for greener solutions for pest management is critically important. The main aim of this study was to evaluate the repellent and insecticidal efficacy of essential oils (EOs) from four species of Myrtaceae plants: Psidium guajava (PG), Eucalyptus robusta (ER), Eucalyptus tereticornis (ET), and Baeckea frutescens (BF) against D. citri and to analyze their chemical composition. GC-MS analysis was performed, and the results indicated that the EOs of PG, ER, ET, and BF were rich in terpenoids, ketones, esters, and alcohol compounds. The repellent rate of all four EOs showed that it decreased with exposure time but increased with concentrations of EOs. Repellent rates ranged from 80.50% to 100.00% after treating D. citri for 6 hours with four EOs at 100% concentration and decreased to 67.71% to 85.49% after 24 hours of exposure. Among the compounds from the EOs tested, eucalyptol had the strongest repellent activity, with a 24-hour repellency rate of 100%. The contact toxicity bioassay results showed that all EOs have insecticidal toxicity to D. citri; the LC50 for nymphs was 36.47-93.15 mL/L, and for adults, it was 60.72-111.00 mL/L. These results show that when PG is used as the reference material, both ER, ET, and BF EOs have strong biological activity against D. citri, which provides a scientific basis for the further development of plant-derived agrochemicals.

This study is based on the BP neural network regression model optimised by genetic algorithm and dedicated to predicting the amount of car purchase, this study is important for all parties in the automotive industry. By plotting the violins of six indicators, including gender, age, annual salary, credit card debt, net worth, and car purchase amount, we found that, except for gender, the other data showed a trend of dense in the middle gradually decreasing to the upper and lower sides, with an overall normal distribution. The correlation analysis shows that the amount of car purchase has a high positive correlation with age (0.63), followed by annual income (0.62), the correlation between net worth and the amount of car purchase is 0.49, while the correlation between credit card debt and the amount of car purchase is low (0.03). In the experimental part, we analyse the predicted-actual values of the training and test sets in comparison and find that the model predicted values are very close to the actual values. The RMSE of the training set is 11.559 and the RMSE of the test set is 11.4835, which is not much different from each other, indicating that the model has good generalisation ability. In addition, the R2 of both the training and test sets is 1, indicating that the model fits well. The MAE of the training set is 8.2512 and the MAE of the test set is 9.578; the MBE of the training set is -4.3035 and the MBE of the test set is -2.4547.These metrics indicate that the model performs well in predicting the amount of car purchase. In conclusion, this study provides an effective and reliable method to predict the amount of car purchase, creating a useful bridge between car manufacturers, dealers and consumers.

This study presents a groundbreaking approach in the field of gaming AI, focusing on the classic game” Pacman” through the lens of DRL integrated with advanced optimization techniques. The core of the research involves adapting DRL models for” Pacman”, utilizing the ESO for hyper- parameter tuning. These novel adaptations significantly enhance the AI agent’s performance, demonstrating a remarkable improvement in adaptability, responsiveness, and efficiency within the game environment. A pivotal aspect of this research is the innovative integration of metaheuristic optimization techniques into the DRL framework, a first in the domain of Atari gaming AI. This integration has proven essential in fine-tuning the DRL models, leading to a more effective and dynamic gaming experience. The study thoroughly evaluates and compares the performance of these algorithms, providing empirical support for their effectiveness and setting a new benchmark in AI-driven game development. The implications of this research extend beyond gaming AI, opening up possibilities for future exploration in several directions. Expanding DRL models to other complex gaming environments, continuous algorithmic enhancements, real-time learning adaptations, and applying these principles to robotics and autonomous vehicles are examples. The research also emphasizes ethical and responsible AI use in gaming to address fairness and addiction issues.

Ten new Schiff bases, including thiocarbohydrazone derivatives (1–10), were synthesized using an efficient, uncomplicated, and environmentally friendly approach using the reaction of alkyl-substituted aldehydes. The thiocarbohydrazones that were obtained were derived from o-phthalaldehyde. The chemical structures of the compounds were identified by the utilization of UV-Vis spectroscopy, Fourier transform infrared (FT-IR), ¹H nuclear magnetic resonance (¹H NMR), ¹³C nuclear magnetic resonance (¹³C NMR), and elemental analysis methods. The thermal stability of thiocarbohydrazones was examined using thermogravimetric analysis (TGA-DSC). Investigations were conducted on urease inhibition tests, pharmacokinetic property research, and molecular docking. The collection of samples with the urease enzyme revealed significant molecular docking findings, with MolDock scores ranging from -153 to -189. The compounds have the potential to be effective drug candidates for treating certain diseases, as indicated by the consistent results of molecular docking, urease inhibition tests, and pharmacokinetic characteristics investigations.

Verification of inclusivity of molecular detection methods gives indications on the reliability of viral infections diagnosis because of the tendency of viral pathogens to undergo sequence variation. This study was aimed to select inclusive probe based reverse transcription quantitative PCR (RT-qPCR) assays for the diagnosis of the most widespread and detrimental viruses infecting honeybees, namely the acute bee paralysis virus (ABPV), the black queen cell virus (BQCV), the chronic paralysis bee virus (CBPV), the deformed wing virus variants A (DWVA) and B (DWVB), and the sacbrood virus (SBV). Therefore, detection methods previously described were re-evaluated in silico for specificity and inclusivity. Based on this evaluation, selected methods were modified or new ones were designed and tested in duplex RT-qPCR reactions. The limits of detection (LODs), effect of multiplexing on sensitivity and viral RNA quantification potential in bees and hive debris were assessed. This study made available diagnostic assays able to detect an increased number of virus variants compared to previously described tests and two viral pathogens in a single PCR reaction.

Coal bottom ash (CBA) is a byproduct of coal-burning power plants. This material can be used as a fine aggregate replacement in concrete to reduce global natural material depletion. Concrete usage is increasing rapidly; subsequently, the industry’s carbon footprint is increasing. Using CBA in the construction industry will reduce the technical and economic problems associated with power plants by reducing solid waste. This project aimed to determine if adding new sustainable materials, such as CBA, to concrete will reduce natural raw material usage, energy consumption, and greenhouse gas emissions while improving performance. This study included two projects: comparing increased CBA content to the two-control project and determining the optimum content based on the compressive strength. The optimum CBA content is 50%, which reduces fine aggregate usage in a concrete mix by 50% while maintaining equivalent or better concrete strength than a pure Portland concrete mix without CBA. The air content of the concrete mix containing % CBA decreased with increased CBA content and compressive strength, possibly due to the spacing factor of the voids in the mix and CBA’s porous and fineness behavior. The CBA optimum content had a unit weight lower than the controls for both projects, which makes the CBA mix a lightweight concrete. Using this material can significantly reduce natural material usage and environmental harm by reducing CBA waste disposal and improving concrete performance.

Mesenchymal stem cells due to their multipotent ability are considered one of the best candidates to be used in regenerative medicine. The most used source of those stem cells long the last decades was the bone marrow, despite its limited cell resources and the painful and very invasive surgical treatment. Therefore, the scientific community investigated many alternative sources for the harvesting of these stem cell subpopulations. Adipose tissue attracted the biggest interest thanks to special features, such as great tissue abundance, easy availability, and in the end the large amount of positive mesenchymal stem cells. Once it was verified that these stem cell subpopulations possess the same bone marrow differentiation potential, it became evident that they would be the ideal candidate to be used in regenerative and tissue engineering applications. Even the regenerative use of these cells long become a sensitive point because not all the acquired phenotypes resulted in the best source for plastic procedures. As documented in the literature, the ideal fillers for regenerative is represented by stem cell population. Therefore, it became crucial to investigate any kind of biological mechanism on the differentiative ability of those mesenchymal stem cells, especially their regulative factors. One of the most studied and important regulators are the miRNAs. Indeed, miRNA play a crucial role in the differentiation process of adipose stem cell cultures isolated from patients or obtained by commercial cell lines toward osteocytes, chondrocytes, and adipocytes differentiated populations.

Given the increased provision of international exchange programmes in higher education, this paper examines students’ perceptions of their learning process in one Erasmus+ programme in Food Innovation and Product Design. Using evaluation model of international exchange programme and triple helix model as the analytical framework, students’ testimonials from 2015–2021 were analysed using a critical discourse analysis. EU and non-EU students had similar perceptions of learning outcomes related to knowledge of the food innovation industry, but non-EU students had a stronger sense of awareness of both the international context and multicultural interaction.

In this report we describe the case of a healthy, young, athletic woman who developed acute lymphoblastic leukaemia (ALL)/lymphoblastic lymphoma (LBL) after receiving the second dose of the Pfizer/BioNTech modified mRNA (modRNA) COVID-19 genetic vaccine (marketed as Comirnaty^®). The first dose of the genetic vaccine did not appear to illicit any noticeable side effects, but within 24 hours of the second dose the patient suffered widespread and intensifying bone pain, fever, vomiting, and general malaise. Due to the persistence of the symptoms, the patient underwent a series of tests and examinations including a full laboratory workup, a consult with a clinical immunologist and rheumatologist, a Positron Emission Tomography (PET) imaging, as well as an osteomedullary biopsy. These together led to a definitive diagnosis of ALL. A time interval of 16 weeks from the second vaccination to the diagnosis of cancer was noted. Several similar cases with identical pathology which developed after the modRNA COVID-19 vaccination, are described in case reports in the scientific literature. The massive and indiscriminate use of genetic vaccines to fight COVID-19 is raising serious concerns about their safety and about the technology platform as a whole for this purpose. Growing evidence is accumulating regarding the biodistribution and persistence of the modRNA which can reach, thanks to the lipid nanoparticles, a multitude of tissues and organs of the body, including the bone marrow and other blood-forming organs and tissues. Moreover, there is evidence that the modRNA vaccines display a particular tropism for the bone marrow, influencing the immune system at multiple levels and being able to trigger not only autoimmune-based pathologies, but also neoplastic mechanisms. The aim of this article is to assess, on the basis of the available scientific literature, the risk of developing haematopoietic cancers after modRNA vaccination, and to investigate the potential genetic mechanisms involved in the pathogenesis of disease.

Efficient triaging and referral assessment are critical in ensuring prompt medical intervention in the Community Health Care (CHC) system. However, the existing triaging systems in many Community Health Services are an intensive, time-consuming process and often lack accuracy, particularly for various symptoms that might represent heart failure or other health-threatening conditions. There is a noticeable limit of research papers describing AI technologies for triaging patients. This paper proposes a novel quantitative data-driven approach using machine learning (ML) modelling to improve the community clinical triaging process. Furthermore, this study aims to employ the feature selection process and machine learning power to reduce the triaging process’s waiting time and increase accuracy in clinical decision-making. The model was trained on medical records of “Heart Failure” patients’ dataset, which included demographics, past medical history, vital signs, medications, and clinical symptoms. A comparative study was conducted using the list of machine learning algorithms where XGBoost demonstrated the best performance among other ML models. The triage levels of 2,35,982 patients achieved an accuracy of 99.94%, precision of 0.9986, recall of 0.9958, and f1-score of 0.9972 within 0.059 seconds. The proposed diagnostic model can be implemented for the CHC decision system and be developed further for other medical conditions.