Articles

This work explores the intimate process of creating an acrylic portrait of a long-time friend, guided by photographs. Through a detailed examination of the technical and emotional aspects of portrait painting, this narrative reflects on the deep bond and shared history between the artist and subject. Emphasising the role of mutual understanding, trust, and empathy in the creative process, the paper illustrates how personal experiences and memories shape artistic expression. Drawing on art therapy, narrative identity, and the psychological impact of art, this study offers insights into how portrait painting can facilitate personal reflection, retrospection, and meaningful connection.

We study a dynamic model of the relationship between two people where the states depend on the “power” in the relationship. We perform a comprehensive analysis of stability of the system, and determine a set of conditions under which stable relationships are possible. In particular, stable relationships can occur if both people are dominant, but the sum of dominances is below a bound determined by the model’s parameters. Stable relationships can also occur if one person is dominant and the other is submissive, provided the level of dominance exceeds the level of submissiveness but not beyond a threshold. We also conclude that a stable relationship is not possible if both people are submissive. While our model is motivated by a social or romantic relationship, it can also be applied to professional or business relationships, diplomatic relationships between nations, and certain biological interactions between organisms and between automated agents or robots.

High-Performance Computing (HPC) is indispensable for large-scale scientific simulations, but achieving optimal performance on modern supercomputers is increasingly challenging. As HPC systems scale toward exascale, they face escalating complexity in hardware, software, and workloads. Traditional optimization methods (manual tuning and heuristic algorithms) struggle to cope with dynamic workloads and intricate system behaviors. Artificial Intelligence (AI) techniques offer a promising approach to address these challenges. This article provides an overview of AI-powered optimization in HPC, focusing on how machine learning and related AI methods enhance performance, efficiency, and scalability of scientific simulations. We survey key AI techniques applied to HPC optimization including machine learning for performance modeling, deep reinforcement learning for resource management, and AI-driven surrogate models for accelerating simulations and illustrate their impact through case studies in domains such as job scheduling and fluid dynamics. We discuss the practical applications of these techniques, highlighting reported performance gains (e.g., substantial reductions in simulation run time and improved resource utilization). We also examine the challenges in integrating AI with HPC (such as training overhead, data movement, and reliability concerns) and outline future directions for research. The convergence of AI and HPC is poised to produce “smart” simulation workflows that intelligently adapt and optimize in real time, pushing the frontiers of scientific computing.

The recent development of Mobile System-on-Chip (SoC) has completely changed the functionality of the contemporary smartphones, wearables, and IoT devices through enhanced computational strength but with stringent power and heat constraints. Mobile applications are becoming more complex (e.g., real-time computer vision, high-resolution imaging, on-device artificial intelligence, etc.) and SoCs have to provide high throughput, good work load distribution, and responsive multitasking without sacrificing battery life. To overcome these issues, modern SoCs are based on heterogeneous multicore designs, with big. LITTLE designs, special purpose accelerators, like NPUs and ISPs, and memory arranged hierarchies and backed with high bandwidth buses. Also, more sophisticated power-management approaches like Dynamic Voltage and Frequency Scaling (DVFS), clock gating, and power gating provide fine-tuning energy management in the active and idle conditions. Most recently, scheduling and thermal sensitivity resource allocation using AI has become a promising approach in increasing real-time refinement and flexibility. This paper gives a detailed discussion of performance-driven and power-efficient design methods in mobile SoCs, including architectural innovations, workload optimization, and built-in AI-assisted energy management. The discussion identifies major research trends, system-level developments, and new challenges defining next-generation mobile computing systems.

Pulsatile blood flow in a subject-specific human aortic arch and its major branches is studied computationally for a peak Reynolds number of 1553 and a Womersley number of 22.74. The aortic geometry is constructed from the CT-scan images of a subject. The aorta has out-of-plane curvature and significant area variation along the flow direction. A physiologically representative pulsatile velocity waveform is applied as boundary condition at the inlet of the aorta. The primary velocity profiles are skewed towards the inner wall of the ascending aorta during the entire cardiac cycle. In the decelerating phase, reverse flow is noted along the inner wall and the magnitude of maximum velocity is about 50 % of the peak flow condition. Flow separation is observed in the inner wall of the ascending aorta during the decelerating and reverse flow phases of the cardiac cycle. In the accelerating phase, however, flow separation does not occur. The major observation of the present work is the existence of complex and asymmetrical vortical flow structures which are not observed either in simple curved pipes or in idealized aortic arch computational studies. The relative strength of the secondary flow with respect to the primary flow is quantified by means of Relative Secondary Kinetic Energy whose highest value is evaluated to be 1.202 occurring near the entrance of the right carotid artery during the maximum reverse flow condition. High values of wall shear stress is observed at distal of the left and right subclavian arteries, the bifurcation of brachiocephalic artery between right subclavian artery and right carotid artery, and proximal inner wall of descending aorta during the cardiac cycle. The wall shear stress at the bifurcations of the branches are low and oscillatory and generally correlates with the preferential sites for atherosclerosis. The flow structures on the aorta wall are explicitly highlighted by the limiting streamlines. The application of limiting streamlines to clearly elucidate the complex on-wall flow structures is one of the key contributions of the present study. During the decelerating and reverse flow phases several critical points are observed on the aortic wall. These complex flow structures vanish during the accelerating phase. The observations made in the present study will be helpful in creating accurate and clinically useful computational models.

In the current study, the eight-bar planar mechanism is balanced by reducing variations in shaking force and moments using Minitab and a genetic algorithm. The objective function and constraint equations are taken into consideration, the mathematical model was developed to optimize the balancing of the planar eight-bar mechanism.A set of weighting factors were taken into consideration in order to determine the ideal values for the design parameters based on the contributions of the X and Y components of the shaking force and the shaking moment. A genetic algorithm was used to find the best design parameters. The results showed that the shaking force and moments decreased by 34.5 and 61% from the initial values, respectively.

The decision to choose between onsite and offsite construction is important in the effort toward sustainable construction. Offsite construction is often promoted as an environmentally friendly approach to construction operations. However, previous studies have shown that there is a lack of clarity on the environmental trade-offs between onsite and offsite construction. Factors that can affect the decision to build onsite or offsite include the availability of a local offsite manufacturing facility, the distance of the offsite factory to the final place of use, the proximity of the site to the local supply of material and labor, etc. This study provides a framework to apply the system dynamic modeling technique to evaluate how various factors can affect the environmental impact of the building construction phase (for onsite or offsite construction methods). The system dynamic model (using Vensim software) that was developed provides a platform that allows users to input variables such as the distance that is expected for transportation of labor, material, and equipment to both the onsite facility and the offsite construction location, factors associated with the use of equipment for construction, the distance needed for transportation of building panels or modules from the offsite facility to the final site, etc. Among other things, the model showed that an increase in the distance from the offsite yard to the final construction site increases the total impacts of transportation of completed modules. An increase in the number of trips for the transportation of material to the onsite construction location increases the total impact of onsite construction. In terms of the environmental impact of construction, none of the two methods of construction gives an absolute superiority over the other. The environmental performance of offsite and onsite depends on various associated factors. It is recommended that building practitioners review various factors that are peculiar to their projects to make an informed decision on the best construction methods.

The article's purpose is twofold. First, we wish to draw attention to the insufficiently known field of continuous-time difference equations. These equations are paradigmatic for modeling complexity and chaos. Even the simplest equation , easily leads to complex dynamics, its solutions are perfectly suited to simulate strong nonlinear phenomena such as large-to-small cascades of structures, intermixing, formation of fractals, etc. Second, in the main body of the article we present a small but very important part of the theory behind the above equation marked by . Just as the discrete-time analog of this equation induces the one-dimensional dynamical system on some interval , so the equation induces the infinite-dimensional dynamical system on the space of functions . In the latter case, not only are the long-term behaviours of solutions critically dependent on the limit behaviour of the sequence (as in the discrete case) but also on the internal structure of as . Assuming to be continuous, we consider the iterations of as the semigroup generated by on the space of continuous maps, and introduce the notion of a limit semigroup for in a wider map space in order to investigate asymptotic properties of . We construct a limit semigroup in the space of upper semicontinuous maps. This enables us to describe both of the aforementioned aspects of our interest around the iterations of.

The healthcare access is fundamental rights for every human being. It is Governments responsibility to provide good healthcare services and infrastructure to its citizen. Since last few decades, Government and healthcare industries are struggling to minimize the adverse events impacting people health due to fake medicine. The world health organization also predicted that 4 out of 10 medicines in developing and poor countries are either fake or potentially adulterated. Counterfeit drugs cost billions of dollars deficit to world economy and reduce research and development (R&D) funds allocation from organizations. Stopping counterfeit medicine into supply chain is main challenge for Government and regulatory authorities. The Government and regulatory authorities are now making stringent guidelines to prohibit criminals and counterfeiters to supply fake medicine in markets. Healthcare industry need stringent regulations and secure technologies provide sage and authentic drugs to patients. The FDA has published the 10 years roadmap to implement the drug traceability in United States. The Healthcare Distribution Alliance (HDA) has also mandated to print several barcodes and human readable data in product packaging hierarchy. The FDA is participating in pilot project with leading pharmaceutical drug manufacturer and wholesales to use blockchain technology in interoperable digital network for securing digital traceability data transfer between authorized trading partners.

Many attempts have been made to improve heat transfer for thermally integrated microchannel reforming reactors. However, the mechanisms for the effects of design factors on heat transfer characteristics are still not fully understood. This study relates to a thermochemical process for producing hydrogen by the catalytic endothermic reaction of methanol with steam in a thermally integrated microchannel reforming reactor. Computational fluid dynamics simulations are conducted to better understand the consumption, generation, and exchange of thermal energy between endothermic and exothermic processes in the reactor. The effects of wall heat conduction properties and channel dimensions on heat transfer characteristics and reactor performance are investigated. Thermodynamic analysis is performed based on specific enthalpy to better understand the evolution of thermal energy in the reactor. The results indicate that the thermal conductivity of the channel walls is fundamentally important. Materials with high thermal conductivity are preferred for the channel walls. Thermally conductive ceramics and metals are well-suited. Wall materials with poor heat conduction properties degrade the reactor performance. Reaction heat flux profiles are considerably affected by channel dimensions. The peak reaction heat flux increases with the channel dimensions while maintaining the flow rates. The change in specific enthalpy is positive for the exothermic reaction and negative for the endothermic reaction. The change in specific sensible enthalpy is always positive. Design recommendations are made to improve thermal performance for the reactor.

To ensure maximum flexibility, service providers offer a variety of computing options with regard to CPU, memory capacity, and network bandwidth. At the same time, the efficient operation of current cloud applications requires an infrastructure that can adjust its configuration continuously across multiple dimensions, which are generally not statically predefined. Our research shows that these requirements are hardly met with today's typical public cloud and management approaches. To provide such a highly dynamic and flexible execution environment, we propose the application-driven autonomic management of data center resources as the core vision for the development of a future cloud infrastructure. As part of this vision and the required gradual progress toward it, we present the concept of composable infrastructure and its impact on resource allocation for multi-cloud environments. We introduce relevant techniques for optimizing resource allocation strategies and indicate future research opportunities [1]. Many cloud service providers offer computing instances that can be configured with arbitrary capacity, depending on the availability of certain hardware resources. This level of configurability provides customers with the desired flexibility for executing their applications. Because of the large number of such prerequisite instances with often varying characteristics, service consumers must invest considerable effort to set up or reconfigure elaborate resource provisioning systems. Most importantly, they must differentiate the loads to be distributed between jobs that need to be executed versus placeholder jobs, i.e., jobs that trigger the automatic elasticity functionality responsible for resource allocator reconfiguration. Operations research reveals that the optimization of resource allocator reconfiguration strategies is a fundamentally difficult problem due to its NP-hardness. Despite these challenges, dynamic resource allocation in multi-clouds is becoming increasingly important since modern Internet-based service settings are dispersed across multiple providers [2].

For years, risk assessment and financial calculations have been based on mathematical, statistical, and actuarial studies of existing and historical data. The manual process of building datasets, processing data, deriving trends, identifying periodicities, and analyzing diagnostics is extremely expensive and time-consuming. With the automation and evolution of data science technologies, organizations are now bringing in niche data, such as unstructured data, which contain more disruptive and precise signals for decision-making—thereby making predictions and derivative valuations more robust. This discussion highlights how investment decision-making and financial ecosystem activities are set to be transformed with the power of technical automation, data, and artificial intelligence. A noted trend in the financial investment sector is that financial valuations are highly predictive and highly non-linear in long-term occurrences. To understand these robust evolving signals and execute profitable strategies upon them, the investment management process needs to be very dynamic, open, smart, and technically deep. However, with current manual processes, reaching a high-end asset prediction still seems like a shot in the dark. In parallel, open and democratically developed financial ecosystems query relatively riskless premium opportunities in high-finance valuation and perception. The process of evolving financial ecosystems or the use of automated tools and data to move to unique frontiers could make high-yield profiting opportunities very safe and entirely riskless. Financial economic theories and realistic approximation models support this.

The first visualizations of the structures of the sheep's reproductive system, whether of the uterus or ovaries, were performed through exploratory laparotomies, laparoscopies or through materials obtained in slaughterhouse, being possible to evaluate only what was happening at that moment in the estrous cycle. With the emergence of ultrasound from the 90s, it became possible to dynamically monitor what happened at each moment of the estrous cycle at different times of the year, without having to slaughter the females. Because it is a non-invasive technique and the stress caused by its use is very low, it is increasingly necessary to use ultrasound in sheep, either for pregnancy diagnosis or for multiplying herds using biotechnologies, which the ultrasound examination becomes indispensable to obtain the best results. This review aims to describe the main advances in the study of the estrous cycle of sheep with the help of ultrasonography and its influence on the productive efficiency of the herd.

First, we propose the nonlinear whole seismology and its three basic laws. Next, based on the nonlinear equations of fluid dynamics in Earth’s crust, we obtain a chaos equation, in which chaos corresponds to the earthquake, and shows complexity on seismology. But, combining the Carlson-Langer model and the Gutenberg-Richter relation, a simplified nonlinear solution and corresponding magnitude-period formula of earthquakes may be derived approximately. Further, we research the topological seismology. From these theories some predictions can be calculated quantitatively and are already tested. Combining the Lorenz nonlinear model, we may discuss the earthquake migration to and fro. Finally, if various modern scientific instruments, different scientific theories and some paranormal ways for earthquake are combined each other, the accuracy of multilevel prediction will be increased.

The evolution of supply chains (SC) from a linear to a network structure created an opportunity for new processes, product/service offerings, and provider-business. Rising customer service expectations have led to the need for innovative SC designs to develop and sustain competitive performance globally. Firms are forced to respond and adapt accordingly, thereby leading to design, network, operational, and performance dynamics. Traditionally, SCs are treated as static structures, focusing solely on design and/or operational optimization. Such perspectives are not viable options for SC domains, as they address only a portion of the dynamic problem space, use a deterministic assumption of dominant design variables, capitalize on past data to predict future decisions, and offer pre-classified forecasting options complemented with a limited comprehension of systemic SC elasticity. Novel self-learning agentic systems are proposed that blend the sciencematics of SC decisions and dynamics. The designs guide firms seeking to build adaptive SCs using operational decision processes. The designs address the agentic nature of SC, embedding computational interaction models of firm SC networks. The designs contrast the stochastic action-taking and thereby the performance outcomes, discovering opportunities for adaptive operational designs of SC tasks. Fine-tuning and meta-learning are new design capabilities that adapt to evolving dynamic environments. Frameworks for behavioral customization and systematic exploration of the design space are provided as user guides. Exemplar designs are also provided to serve as a translation template for users to express operational models of their own contexts. To account for the dynamics of supply chains (SC), agent-based models are increasingly adopted. Such models exhibit SC structure and/or formulation dynamics. Though existing efforts commence adjacent-only structural changes, dynamism with respect to tasks is crucial for SC design and operational strategy development. Proposed is a process modeling library and workflow for discovering intricate designs of adaptive agentic systems. The library revises Dataflow and Structure, concealing sequencing and context designs of processes. Prompted specifications describe and enact designs. Applications in SC formulation discovery are provided.

Enterprise databases are the heart of applications and contain the most sensitive and critical information of organizations. While there have been significant advances in the security of databases, vulnerabilities still exist due to mistakes made by application developers, database administrators, and users. Manual detection and patching of such vulnerabilities typically take months, but an automated detection and remediation framework is proposed to fill the gap and eliminate a significant number of these vulnerabilities in near-real time. This framework comprises two key components: a detection engine that leverages static analysis to identify potential patches, coupled with query dynamic testing and fuzzing to identify exploitable misconfigurations; and an orchestration engine that applies detected patches on the database, validates the accuracy of the fix, and rolls back changes if the problem is not resolved. A prototype of this framework has been implemented and validated on a real-time database deployed in an enterprise environment. Because of the complexity of the problem landscape, the research focus is on static vulnerability detection and automated corrective actions. These two capabilities can greatly reduce the manual workload associated with vulnerability detection and significantly enhance the assurance that the granted privileges validate the least privilege principle. The proposed architecture aims to enable the deployment of a detection-and-remediation solution that minimizes human effort, reduces the enterprise-at-risk window, and maximizes the volume of detected vulnerabilities.

Biopharmaceuticals, or biologics, are a burgeoning sector in the pharmaceutical industry, predicted to reach $239.4 billion by 2025. This unparalleled growth is often attributed to the enhanced specificity offered by large molecules over small molecules. The large size of the constituent proteins necessitates the continuous implementation of big data predictive analytics to elucidate the most effective candidates in the lead optimization process. These same methodologies can be applied, and with the advent of machine learning and automated predictive analytics, this is becoming an increasingly facile task, to the augmentation and optimization of the downstream production processes that comprise the majority of the development cost of any biologic. In this work, big data from cell line generation, product and process design, and large-scale lead validation studies have been used to compare the applicability of simple statistical models against these black-box approaches for the rapid acceleration of enzymes to the pilot plant stage. This research can be expanded upon to exploit the big datasets generated as part of the progression of biologics through the development pipeline to further optimize production outcomes. Over the coming months, data from the project will be used to probe which approaches are amenable to which processes and, as a result, more amenable to various economic simulations. The computed optimization objective for the HIT must include the cost of acquiring, storing, and analyzing data to construct these predictive models, alongside the expected commercial reward of choosing an optimally ranked candidate. In this vein, perspective must be taken in the probable future price, capability outputs, and ownership issues of increasingly sophisticated data analysis software as superstructures become more frequent. It is frequently stated that decisions made to reduce production costs are data-driven, but that is not because more economically or energetically costly experiments or production methods are employed; to truly evaluate production steps, dynamic energy, and economic models need to become more commonplace. Conversion of process quality approaches from large questionnaires, risk analysis, and expert opinion-driven methods to statistical and thus more reliable approaches is an area of future research in analytics used herein.