In the past twenty years, the IT industry has moved away from using physical servers for workload management to workloads consolidated via virtualization and, in the next iteration, further consolidated into containers. In the next step, container workloads based on Docker and Podman as underlying container technologies were orchestrated/automated via Kubernetes or OpenShift. On the other hand, high-performance computing (HPC) environments have been lagging in that process, as there’s still much work to figure out how to apply containerization platforms for HPC in real-life scenarios. Kubernetes and OpenShift have many advantages – generally speaking, container technologies use quite a bit less overhead from the computing perspective while providing many benefits in flexibility, modularity, and maintenance. Therefore, they are ideal for tasks requiring a lot of computing power. There are also some tradeoffs regarding the complexity of these two platforms - they’re just not that user-friendly when used by people without years of experience managing them. In this paper, we propose a different architecture based on seamless hardware integration and user-friendly, dynamic workload placement based on real-time performance analysis and prediction coupled with Machine Learning-based scheduling.

The Industrial Internet of things (IIoT) enabled smart system has entered into a golden era of rapid technology growth. IIoT is a concept to make every system interrelated such that they are able to collect and transfer data over a wireless network without human intervention. In this paper, we discuss the development of an IoT enabled system to monitor the vibration signature of equipment as part of prognosis and availability management system (P&AM) that serves to prevent unplanned operation downtime and catastrophic failure of a whole system. In order to simply the complexity of processing video content and performing inference, the Intel OpenVINO platform was selected because of it’s simplicity, portability across Intel AI processors, performance and comprehensiveness of it’s analytical and diagnostics capabilities that can be tested in Intel’s DevCloud. The IIoT system consists of a High Performance Computing (HPC) platform based on Intel’s Xeon processors and Movidius AI accelerator, Intel’s OpenVINO toolkit for AI, a Regul high performance programmable controller capturing vibration data through sensors and a low-latency network connection. Notifications of anomalies are sent to a smartphone. This paper reveals an approach for the features extraction and selection, known as feature engineering, of the equipment component we want to protect. Feature engineering is the first step for the P&AM of these components and extends to the whole system. The broader aim of this paper is to help technical leaders at the exploring or experimenting stages of their AI framework to learn the concepts of implementing algorithms using datasets that have real value to their companies. Datasets generated and referred to in this paper were generated by simulation under various material failure scenarios.

Implementing High-Performance Computing to solve In-Memory Data Grid (IMDG)-based energy infrastructure resilience research problems in a heterogeneous environment presents a challenge to workflow management systems. Large-scale energy infrastructure needs multi-variant planning and tools to allocate and dispatch resources in IMDG taking into account the subject domain specificity, resource characteristics, and constraints and quotas for resource use. To that end, we propose a workflow management system using our Orlando Tools framework. To scale computing resources, we provide their integration and use corresponding software to determine key application parameters that can significantly impact the processed data size and the required number of allocated resources. We automated the IMDG cluster launch for the workflow executions. To demonstrate the advantage of our solution, we apply it to evaluate the resilience of the existing energy infrastructure model. Compared to similar approaches, our approach explores large infrastructures by modeling many simultaneously failed elements of different types up to the number of network elements. In terms of problem-solving efficiency and resource use, we achieve near-linear speedup with increasing the number of nodes of each resource.

Top-down models are defined by hardware architects to provide information on the utilization of the different hardware components. The target is to isolate the users from the complexity of the hardware architecture while giving them insight into how efficiently the code is using the resources. In this paper, we explore the applicability of 4 top-down models defined for different hardware architectures powering state-of-the-art HPC clusters (Intel Skylake, Fujitsu A64FX, IBM Power9, and Huawei Kunpeng 920) and propose a model for AMD Zen 2. We study a parallel CFD code used for scientific production to compare these 5 Top-Down models. We evaluate the level of insight achieved, the clarity of the information, the ease of use, and the conclusions that each one allows us to reach.

Big data has revolutionised science and technology leading to the transformation of our societies. High Performance Computing (HPC) provides the necessary computational power for big data analysis using artificial intelligence and methods. Traditionally HPC and big data had focused on different problem domains and had grown into two different ecosystems. Efforts have been underway for the last few years on bringing the best of both paradigms into HPC and big converged architectures. Designing HPC and big data converged systems is a hard task requiring careful placement of data, analytics, and other computational tasks such that the desired performance is achieved with the least amount of resources. Energy efficiency has become the biggest hurdle in the realisation of HPC, big data, and converged systems capable of delivering exascale and beyond performance. Data locality is a key parameter of HPDA system design as moving even a byte costs heavily both in time and energy with an increase in the size of the system. Performance in terms of time and energy are the most important factors for users, particularly energy, due to it being the major hurdle in high performance system design and the increasing focus on green energy systems due to environmental sustainability. Data locality is a broad term that encapsulates different aspects including bringing computations to data, minimizing data movement by efficient exploitation of cache hierarchies, reducing intra- and inter-node communications, locality-aware process and thread mapping, and in-situ and in-transit data analysis. This paper provides an extensive review of the cutting-edge on data locality in HPC, big data, and converged systems. We review the literature on data locality in HPC, big data, and converged environments and discuss challenges, opportunities, and future directions. Subsequently, using the knowledge gained from this extensive review, we propose a system architecture for future HPC and big data converged systems. To the best of our knowledge, there is no such review on data locality in converged HPC and big data systems.

Cloud computing refers to applications delivered as services over the Internet. Cloud systems employ policies that are inherently dynamic in nature and that depend on temporal conditions defined in terms of external events, such as the measurement of bandwidth, use of hosts, intrusion detection or specific time events. In this paper, we investigate an optimized resource management scheme named v-Mapper. The basic premise of v-Mapper is to exploit application-awareness concepts using software-defined networking (SDN) features. This paper makes three key contributions to the field: (1) We propose a virtual machine (VM) placement scheme that can effectively mitigate the VM placement issues for data-intensive applications; (2) We propose a validation scheme that will ensure that a service is entertained only if there are sufficient resources available for its execution and (3) We present a scheduling policy that aims to eliminate network load constraints. An evaluation was carried out with various benchmarks and demonstrated that v-Mapper shows improved performance over other state-of-the-art approaches in terms of average task completion time, service delay time and bandwidth utilization. Given the growing importance of supporting large-scale data processing and analysis in datacentres, the v-Mapper system has the potential to make a positive impact in improving datacentre performance in the future.

As time progresses and technology improves, biological data sets are continuously increasing in size. New methods and new implementations of existing methods are needed to keep pace with this increase. In this paper, we present a high performance computing(HPC)-capable implementation of Iterative Random Forest (iRF). This new implementation enables the explainable-AI eQTL analysis of SNP sets with over a million SNPs. Using this implementation we also present a new method, iRF Leave One Out Prediction (iRF-LOOP), for the creation of Predictive Expression Networks on the order of 40,000 genes or more. We compare the new implementation of iRF with the previous R version and analyze its time to completion on two of the world's fastest supercomputers Summit and Titan. We also show iRF-LOOP's ability to capture biologically significant results when creating Predictive Expression Networks. This new implementation of iRF will enable the analysis of biological data sets at scales that were previously not possible.

Educational institutions must identify students who are academically struggling to provide them with necessary support to improve their performance. In this context, recommendation systems powered by deep learning techniques are vital for detecting and categorizing such students. These systems help students plan their future by uncovering patterns in their historical academic data. This study introduces a new deep learning model designed to classify academically underperforming students in educational settings. The model incorporates a Gated Recurrent Neural Network (GRU) and includes specific neural network features like a dense layer, max-pooling layer, and the ADAM optimization algorithm. The model's training and evaluation were conducted using a dataset comprising 15,165 student assessment records from various academic institutions. The performance of the developed GRU model was benchmarked against other educational recommendation systems, including Recurrent Neural Network models, AdaBoost, and the Artificial Immune Recognition System v2. The proposed GRU model demonstrated remarkable accuracy, achieving an overall rate of 99.70%.

Reservoir computing is a machine learning method that uses the response of a dynamical system to a certain input in order to solve a task. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.

This paper thoroughly assesses the ARM and RISC-V architectures in the context of High-Performance Computing (HPC). It includes an analysis of Docker, Kubernetes, and KVM virtualization integration. Our study aims to evaluate and compare these systems' performance, scalability, and practicality in a general context and then assess the impact that might have on special use cases, like HPC. ARM-based systems exhibited better performance and seamless integration with Docker and Kubernetes, underscoring their advanced development and effectiveness in managing high-performance computing workloads. On the other hand, despite their open-source architecture, RISC-V platforms presented considerable intricacy and difficulties in coordinating with Kubernetes, which hurt their overall effectiveness and ease of management. The results of our study offer valuable insights into the practical consequences of implementing these architectures for HPC, highlighting ARM's preparedness and the potential of RISC-V while acknowledging the increased complexity involved and significant trade-offs at this point.

UHPC is a type of cement-based composite used in new construction and/or rehabilitation of existing buildings to extend service life. It is a novel composite material that can serve as an alternative to concrete construction in hostile climates. Following decades of study and production, a diverse variety of commercial UHPC compositions are now available globally to meet the rising number of applicants and demand for high-quality building materials. Although UHPC offers major benefits over normal concrete, its utilization is restricted due to restrictive design rules and exorbitant costs. As a result, a thorough examination of the durability properties of UHPC is required to give important information for material testing requirements and processes, as well as to broaden its practical uses. This report is aimed at increasing basic understanding of UHPC and supporting more UHPC research and applications.

Cloud computing is a strong and efficient technology facing some efficiency challenges attributable to its decentralized nature. The emergence of fog computing has subdivided the expansive cloud into numerous smaller units known as "cloudlets." Fog computing is one of the most recent advancements in today’s cloud computing landscape. Each cloudlet is a novel and diminutive architectural element extending the contemporary cloud computing infrastructure. The term "cloudlets" has been integrated with fog computing due to its augmenting impact on the attributes of cloud computing. F fog computing is a significant foundation and an indispensable cloud component. Consequently, this technology holds the potential to address efficiency concerns effectively. As a result, this paper focuses on enhancing the efficiency of the cloud computing environment by fine-tuning the efficiency parameters derived from fog computing. These parameters encompass response time, latency, and network utilization. The proposed solution involves developing a simulation system to minimize task redundancy and increase performance levels in the healthcare environment. The outcomes manifest a substantial improvement in the performance of the suggested system, with a clear increase depending on the three key parameters (response time, network utilization, and latency). Therefore, these findings are encouraging and carry significant implications, promoting the application of the proposed system in the real healthcare environment.

Approximate Computing (AxC) has emerged as a promising paradigm to enhance performance and energy efficiency by allowing a controlled trade-off between accuracy and resource consumption. It is extensively adopted across various abstraction levels, from software to architecture and circuit levels, employing diverse methodologies. The primary objective of AxC is to reduce energy consumption for executing error-resilient applications, accepting controlled and inherently acceptable output quality degradation. However, harnessing AxC poses several challenges, including identifying segments within a design amenable to approximation and selecting suitable AxC techniques to fulfill accuracy and performance criteria. This survey provides a comprehensive review of recent methodologies proposed for performing Design Space Exploration (DSE) to find the most suitable AxC techniques, focusing on both hardware and software implementations. DSE is a crucial design process where system designs are modeled, evaluated, and optimized for various extra-functional system behaviors such as performance, power consumption, energy efficiency, and accuracy. A systematic literature review was conducted to identify papers that ascribe their DSE algorithms, excluding those relying on exhaustive search methods. This survey aims to detail the state-of-the-art DSE methodologies that efficiently select AxC techniques, offering insights into their applicability across different hardware platforms and use-case domains. For this purpose, papers were categorized based on the type of search algorithm used, with Machine Learning (ML) and Evolutionary Algorithms (EAs) being the predominant approaches. Further categorization is based on the target hardware, including Field-Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), general-purpose Central Processing Units (CPUs), and Graphics Processing Units (GPUs). A notable observation was that most studies targeted image processing applications due to their tolerance for accuracy loss. By providing an overview of techniques and methods outlined in existing literature pertaining to the DSE of AxC designs, this survey elucidates the current trends and challenges in optimizing approximate designs.

Real-time data processing and decision-making are increasingly crucial in various applications, driven by the continuous influx of data streams. Event-driven AI workflows within serverless computing environments offer a promising approach to handle these real-time demands efficiently. This paper presents a framework for simulating and analyzing the performance characteristics of such workflows. Our proposed approach utilizes simulated data with varying event rates and durations to investigate the impact on key performance metrics like latency, throughput, and resource utilization. This enables a comprehensive evaluation of the inherent trade-offs within event-driven AI systems. The key findings reveal a trade-off between latency and throughput. As the event rate increases, average processing latency generally increases while average throughput increases. Resource utilization remains relatively stable across different event rates in the simulated scenarios (e.g., 75.55\% at 2 events/second, 74.51\% at 10 events/second). This framework provides a valuable tool for understanding the performance characteristics of event-driven AI workflows and optimizing resource allocation strategies.

This research focuses on the Hopf bifurcation occurring at a singular point with high-order degeneracy in a class of three-dimensional systems. Based on center manifold theorem, by expanding the technique for determining singular point values of degenerate singularities in two-dimensional systems, the formal series method to determine the singular point values at the high-order degenerate critical point is discussed. Furthermore, a class of specific three-dimensional differential systems with high-order degenerate singularities is explored. By computing the singular point values and determining center conditions, we proved that a minimum of 5 small-amplitude limit cycles in the vicinity of the origin. This research provides new perspectives on the center problem and limit cycle bifurcation in high-dimensional systems with high-order degenerate singular points.

Software quantum simulators are the most accessible tools for designing and testing quantum algorithms. This paper presents a comprehensive approach to building a software-based quantum simulator based on classical computing architectures. We explore fundamental quantum computing concepts, including state vector representations, quantum gates, and memory management techniques. The simulator prototype implements various memory optimization strategies, such as full-state representation, dynamic state pruning, and shared memory parallelization with OpenMP and distributed memory models using MPI. Additionally, data compression techniques, like ZFP, are explored to enhance simulation performance by reducing memory footprint. The results are validated through performance comparisons with leading open-source quantum simulators, such as Intel-QS, QuEST, and qsim. Our findings highlight the trade-offs between computational overhead and memory efficiency. This demonstrates that a hybrid approach using distributed memory and compression offers the best scalability for simulating large quantum systems. This work provides a foundation for developing efficient quantum simulators supporting more complex quantum algorithms on classical hardware.

The giant challenge breeding flood-irrigated rice is to identify superior genotypes that present high-yielding with specific grain qualities, resistance to abiotic and biotic stresses, excellent adaptation to the target environment. Thus, the objectives of this study were to propose a bayesian multi-trait model, estimate genetic parameters, and select flood-irrigated rice genotypes with better genetic potentials in different evaluation environments. For this, twenty-five rice genotypes belonging to the flood-irrigated rice improvement program were evaluated. The grain yields, grain length, width and thickness, grain length, and grain width and weight of 100 grains in the agricultural year 2016/2017. The experimental design used in all experiments was a randomized block design with three replications. The Monte Carlo Markov Chain algorithm estimated genetic parameters and genetic values. The grain thickness trait was considered highly heritable, with a credibility interval ranging from: h^2: 0.9480; 0.9440; 0.8610, in environments 1, 2, and 3, respectively. The grain yields showed a low correlation estimate between grain thickness and 100-grain weight, in all environments, with a credibility interval ranging from (ρ= 0.5477; 0.5762; 0.5618 and 0.5973; 0.5247; 0.5632, grain thickness and 100-grain weight, in environments 1, 2, and 3, respectively). The Bayesian multi-trait model proved to be an adequate strategy for the genetic improvement of flood-irrigated. Genotypes 2 and 15 had similar potential in the three environments, they should be selected as high-performance multi-trait genotypes for the genetic breeding of flood-irrigated rice in the program.

High-Intensity Functional Training (HIFT) is a novel exercise intervention that may test body systems in a balanced and integrated fashion by challenging individuals’ abilities to complete mechanical work. However, research has not previously determined if physical work capacity is unique to traditional physiologic measures of fitness. Twenty-five healthy men and women completed a six-week HIFT intervention with physical work capacity and various physiologic measures of fitness assessed pre- and post-intervention. At baseline, these physiologic measures of fitness (e.g., aerobic capacity) were significantly associated with physical work capacity and this relationship was even stronger at post-intervention assessment. Further, there were significant improvements across these physiologic measures in response to the delivered intervention. However, the change in these physiologic measures failed to predict the change in physical work capacity induced via HIFT. These findings point to the potential utility of HIFT as a unique challenge to individuals’ physiology beyond traditional resistance or aerobic training. Elucidating the translational impact of increasing work capacity via HIFT may be of great interest to health and fitness practitioners ranging from strength/conditioning coaches to physical therapists.

The purpose of this study was to establish the association between total and segmental body composition (BC) variables and anaerobic performance and to create optimal models that best predict such performance in CrossFit® (CF) athletes. Fifty athletes, 25 males and 25 females (age: 33.26 ± 6.81 years; body mass: 72.57 ± 12.17 kg; height: 169.55 ± 8.71 cm; BMI: 25.06 ± 2.31 kg·m−2) were recruited to participate and underwent BC analysis using dual-energy X-ray absorptiometry (DXA) and an all-out laboratory test on a cycle ergometer (Wingate) to determine their anaerobic performance. The results show a significant correlation between BC values and performance, ranging from moderate (r = -0.34, p = 0.015) to near-perfect (r = 0.96, p < 0.01). Furthermore, the created performance prediction models exhibited predictive capacities ranging from 19% (p = 0.017) to 93% (p < 0.001). All prediction models were created using total or segmental lean mass variables, excluding others. BC and performance variables found significant differences between males and females. The findings demonstrate that BC variables are crucial indicators of anaerobic performance in CF athletes. Professionals responsible for athlete performance should use this information to monitor athletes and design training programs.

The application of photopolymerization 3D printing technology in the field of piezoelectric ceramics can achieve high precision, customization, and personalized design of piezoelectric ceramics, providing a better technical means for the fine processing of ceramic materials. We have studied the preparation of PZT piezoelectric ceramic 3D printing slurry, as well as the analysis of forming errors of printed green bodies, debinding process, and sintering. We successfully printed and sintered various porous honeycomb structures of D-type and G-type using a PZT slurry with a solid content of 55 wt.%.

(1) Background: The purpose of this study was to assess the external load demands in futsal, con-sidering both home and away matches and their outcomes, in order to plan microcycles throughout the season based on the external load of each match. (2) Methods: The external load of 10 players from a First Division team in the Spanish Futsal League was recorded. The total dura-tion of player involvement with the clock stopped was 18.15 ±7.04 minutes. The players' external load was monitored using OLIVER devices. To analyse the influence of the match outcome and location on the external load, a univariate general linear model (GLM) analysis was conducted. (3) Results: There are no differences between the variables, except for accelerations of 2 to 3 m/s² (m) per minute and the number of accelerations of 2 to 3 m/s² per minute. Despite not showing significant differences, there is a trend indicating a higher demand for external loads in away matches as well as in matches won. (4) Conclusions: These findings are important for planning microcycles and providing the appropriate dosage to each player to achieve optimal performance in matches.

Background: Restorative materials might significantly affect load transmission at peri-implant bone. The aim of the present study is to evaluate the shock absorption capacity of two different polymeric materials to be used as frameworks for implant-supported prostheses. Methods: A masticatory robot was used to compare the shock absorption capacity of polyether-ketoneketone (PEKK) Pekkton®ivory (Cendres+Mètaux) and glass fiber-reinforced composite (GFRC) TRINIATM (Bicon). Five identical sample crowns for each of the materials was tested un-der the same conditions described in previously published papers (Menini et al. 2013). Forces transmitted at the simulated periimplant bone were recorded and statistically analysed. Results: The statistical analysis of forces transmitted at the simulated dental implant revealed significant differences between all the materials tested and significant differences with the mate-rials tested in a previously published paper (Menini et al. 2013: zirconia, glass ceramic, composite resin, acrylic resin). Only differences between PEKK and veneered PEKK and between PEKK and one of the previously tested composite resins were not statistically significant. PEKK samples demonstrated significantly greater shock absorption capacity compared to GFRC. Conclusions: PEKK revealed optimal shock absorption capacity. Further studies are needed to evaluate its efficacy in case of long span prostheses with reduced prosthetic volume.

UHPC is a cement-based composite that is used in new construction and/or renovation of existing structures to increase their service life. It is a unique composite material that may be used as an alternative to concrete in harsh conditions. Following decades of research and development, a wide range of commercial UHPC compositions are now accessible across the world to fulfill the growing number of applications and demand for high-quality construction materials. Although UHPC has significant advantages over conventional concrete, its use is limited because of rigid design restrictions and excessive pricing. As a consequence, a detailed analysis of UHPC's durability qualities is necessary to provide critical information for material testing requirements and methods, as well as to widen its practical applications. The goal of this study is to learn more about UHPC and to encourage more research and use of UHPC.

The complexity of the design in high-rise residential projects is a challenge for the construction industry in completing projects that fit the needs of users. Performance-Based Building Design (PBBD) appears as a design concept that can describe these needs into performance requirements. In this case designing a building can be considered as an iterative process of exploration, where desired functional properties can be created, the shapes are suggested, and evaluation processes is used, so as to bring together the shapes and functions of the building. This concept is a container for designers to produce high-performance buildings. This study aimed to identify the performance-based building design factors applied by architect designers and engineers in high-rise residential building in Surabaya. As part of this study, primary data was collected based on surveys conducted through observation and questionnaire distributed to designers who had or were involved in the high-rise residential design process in Surabaya. A total of sixty-eight respondents were included in this study. Descriptive analysis through a mean and standard deviation scatter plot was used to rank the application of PBBD. Meanwhile, factor analysis was used in the analysis of PBBD application factors. From the results of the analysis, four factors were obtained for the application of PBBD in high-rise residential buildings in Surabaya, namely; the interests of occupants, the sustainability of building operations, the design collaboration process, and the risk of loss. Future research is the influence relationships and measure the success model of PBBD at a higher level into BIM (Building Information Modeling) interoperability.

This study analyses some of the leading technologies for the construction and configuration of IT infrastructures to provide services to users. For modern applications, guaranteeing service continuity even in very high computational load or network problems is essential. Our configuration has among the main objectives of being highly available (HA) and horizontally scalable, that is, able to increase the computational resources that can be delivered when needed and reduce them when they are no longer necessary. Various architectural possibilities are analysed, and the central schemes used to tackle problems of this type are also described in terms of disaster recovery. The benefits offered by virtualisation technologies are highlighted and are bought with modern techniques for managing Docker containers that will be used to build the back-end of a sample infrastructure related to a use-case we have developed. In addition to this, an in-depth analysis is reported on the central autoscaling policies that can help manage high loads of requests from users to the services provided by the infrastructure. The results we have presented show an average response time of 21.7 milliseconds with a standard deviation of 76.3 milliseconds showing excellent responsiveness. Some peaks are associated with high-stress events for the infrastructure, but the response time does not exceed 2 seconds even in this case. The results of the considered use case studied for nine months are presented and discussed. In the study period, we improved the back-end configuration and defined the main metrics to deploy the web application efficiently.

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things computing. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSN. Then, using the CUDA Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes’ diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services.

In recent times, the adoption of cloud computing by small and medium-sized 9 enterprises (SMEs) has become a crucial issue. These businesses face substantial difficulties in 10 accessing reliable and affordable IT infrastructure due to economic constraints and technological 11 restrictions. Hybrid cloud computing solutions which incorporate Infrastructure as a Service 12 (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) offer a feasible approach to 13overcoming these challenges. This study proposes a systematic literature review of cloud 14 computing's impact on SME performance emphasizing cost-efficiency, reliability, and 15 competitive advantages, using the 2020 Preferred Reporting Items for Systematic Reviews and 16 Meta-Analyses (PRISMA) framework. The inclusion criteria are bounded by (1) publication date 17 between 2014 and 2024, (2) English-language publications, (3) research focused on cloud 18 computing for SMEs, and (4) studies with a clear analytical framework for evaluating SME 19 performance. Following this process, 90 eligible research studies were included. The average 20 total cost for cloud services varies significantly, with operational costs reflecting the scalability 21 of the services and the specific needs of SMEs. This analysis offers valuable guidance for SMEs 22 and stakeholders in optimizing cloud-based strategies for enhanced performance.

The layered oxyselenide BiCuSeO has garnered significant interest because of its ability to exhibit low thermal conductivity and a high Seebeck coefficient. This study involved the preparation of Bi1−xNaxCuSeO1−xFx(x=0, 0.05, 0.10, 0.15, and 0.20) ceramics through the processes of high energy ball milling and cold isostatic pressing. A systematic study was conducted to investigate the impact of co−doping Na/F on the thermoelectric performance of BiCuSeO ceramics. Replacing Bi3+ with Na+ results in the introduction of a considerable amount of holes, leading to a notable enhancement in the electrical conductivity and power factor. The conductivity was significantly increased from 9.10 S cm−1 in the pure BiCuSeO to 94.5 S cm−1 in Bi0.85Na0.15CuSeO0.85F0.15 at 323 K. At 823 K, the power factor of the Bi0.85Na0.15CuSeO0.85F0.15 sample achieved 44.8×10−5 W/ m K2. Moreover, the Bi1−xNaxCuSeO1−xFx ceramics exhibit a minimum thermal conductivity of 0.43 W m−1 K−1. As a result, the Bi0.85Na0.15CuSeO0.85F0.15 sample achieves a maximum ZT value of 0.78, which is 7.09 times greater than that of the pure BiCuSeO sample (0.11) .

Dicarbonyls are electrophiles able to generate the so-called advanced glycation end-products (AGEs). Human exposure occurs via ingestion (e.g., food), inhalation (e.g., electronic cigarettes) and dysregulation of endogenous metabolic pathways (e.g., glycolysis). Biological systems can prevent AGEs formation via enzymatic and nonenzymatic reactions. However, the continuous exposure to dicarbonyls may cause the depletion of endogenous substrates and the beginning of AGEs accumulation (i.e., carbonylation). Since this process has been associated with multiple disorders, several studies advocated the use of dicarbonyl binders as food preservatives or as drugs aimed at mitigating carbonylation damages. As reported for other toxicants (e.g., HNE, acrolein), the screening of selective and potent binders can be performed by measuring the residual amount of a selected dicarbonyl substrate, upon its incubation with candidate binders. Unfortunately, naturally occurring dicarbonyls are poorly responsive to many detectors (e.g., UV or MS). For this reason, most of the screening methods that have been reported to date relies on indirect quantification upon derivatization. However, this strategy can potentially lead to inaccurate results since the tested compounds can affect the derivatization yield. Therefore, we herein reported the use of benzylglyoxal (BGO) as a convenient, UV responsive, dicarboyl surrogate. By using BGO as a substrate, we setup an easy and cheap chromatographic assay for the direct measurement of the binding activity towards dicarbonyls, without derivatization. The method was qualified by assessing the binding ability of some known dicarbonyl binders, obtaining results consistent with literature.

Large-scale land cover plays a crucial role in global resource monitoring and management, as well as research on sustainable development. However, the complexity of the mapping process, coupled with significant computational and data storage requirements, often leads to delays between data processing and product publication, creating challenges for dynamic monitoring of large-scale land cover. Therefore, improving the efficiency of each stage in large-scale land cover mapping and automating the mapping process is currently an urgent and critical issue that needs to be addressed. We propose a high-performance automated large-scale land cover mapping framework(HALF) that introduces high-performance computing technology to the field of land cover production. HALF optimizes key processes, such as automated sample point extraction, sample-remote sensing image matching, and large-scale classification result mosaic and update. We selected several 10°×10° regions globally and the research makes several significant contributions:(1)We design HALF for land cover mapping based on docker and CWL-Airflow, which solves the heterogeneity of models between complex processes in land cover mapping and simplifies the model deployment process. By introducing workflow organization, this method achieves a high degree of decoupling between the production models of each stage and the overall process, enhancing the scalability of the framework. (2)HALF propose an automatic sample points method that generates a large number of samples by overlaying and analyzing multiple prior products, thus saving the cost of manual sample selection. Using high-performance computing technology improved the computational efficiency of sample-image matching and feature extraction phase, with 10 times faster than traditional matching methods.(3)HALF propose a high-performance classification result mosaic method based on the idea of grid division. By quickly establishing the spatial relationship between the image and the product and performing parallel computing, the efficiency of the mosaicking in large areas is significantly improved. The average processing time for a single image is around 6.5 seconds.

The purpose of the present study was to compare heart rate (HR), blood lactate and training load between different CrossFit® workouts, with equalized total volume in men and women. The study included 23 individuals (13 men and 10 women) experienced in CrossFit® training, who performed two workouts with different training types (as many reps as possible - AMRAP and for time) but equalized volume. Measurements of lactate, HR and rating of perceived exertion (RPE) were performed. The results showed that there was no HR interaction between workout time and sex (p = 0.822; η2 = 0.006] and between workout type and sex (p = 0.064, η2 = 0.803). HR significantly differed during each workout type (p <0.001, η2 = 0.621), but not between the two workout types (p = 0.552, η2 = 0.017). Lactate showed no difference between the workout types (p = 0.474, η2 = 0.768), although the training load was higher (p = 0.033, η2 = 0.199) in women when they performed AMRAP. Altogether, HR was not significantly different between training types or sex, while RPE, lactate and training load showed small differences depending on the group (women or men) or workout type (AMRAP or 'for time').

Wind turbine upgrades have been spreading in the recent years in the wind energy industry, with the aim of optimizing the efficiency of wind kinetic energy conversion. This kind of interventions has material and labor costs and it is therefore fundamental to estimate realistically the production improvement. Further, the retrofitting of wind turbines sited in harsh environments might exacerbate the stressing conditions to which wind turbines are subjected and consequently might affect the residue lifetime. This work deals with a case of retrofitting: the testing ground is a multi-megawatt wind turbine from a wind farm sited in a very complex terrain. The blades have been optimized by installing vortex generators and passive flow control devices. The complexity of this test case, dictated by the environment and by the features of the data set at disposal, inspires the formulation of a general method for estimating production upgrades, based on multivariate linear modeling of the power output of the upgraded wind turbine. The method is a distinctive part of the outcome of this work because it is generalizable to the study of whatever wind turbine upgrade and it is adaptable to the features of the data sets at disposal. In particular, applying this model to the test case of interest, it arises that the upgrade increases the annual energy production of the wind turbine of an amount of the order of the 2%. This quantity is of the same order of magnitude, albeit non-negligibly lower, than the estimate based on the assumption of ideal wind conditions. Therefore, it arises that complex wind conditions might affect the efficiency of wind turbine upgrades and it is therefore important to estimate their impact using data from wind turbines operating in the real environment of interest.

With the existence of several programming languages such as C/C++, Java, C#, LISP, Prolog, Python, Simula, F#, Go, Haskell, Scala, Ruby, Dart, Swift, Groovy etc. and diverse paradigms like structured, object-oriented, list, aspect-oriented, service-oriented, web, mobile and logic programming, there is a need to perform an exhaustive comparative analysis of diverse compilers and environments before making a choice of implementation technology in software engineering. Optimization of compilers helps to reduce execution time by making use of high speed processor registers, thereby, eliminating redundant computation. This paper reports some series of performance analysis done with some popular programming languages including Java, C++, Python and PHP. Programs involving recursive and iterative functions like factorial of large numbers and binary search of large arrays were run on the various platforms with the execution time recorded in milliseconds and represented in a chart. This can aid in making a selection of the appropriate language to use for a given application domain.

We present a design of electrostatic comb driver to improve and investigate its actuation performance. The proposed electrostatic comb actuator (ECA) is composed of two set of interdigitated comb microstructures, the inner combs are fixed to form the fish-bone microstructures and the outer combs are movable and connected to crab-leg flexure beams microstructures. The ECA is investigated to optimize the actuation performance by changing the comb shape and the intersection angle of crab-leg flexure beams. The comb shapes are compared between the traditional rectangle shape and the trapezoid shape, while the intersection angles of crab-leg flexure beams are compared between 45°, 60°, and 90° on the influences of ECA displacement and stability characteristics. These parameters are investigated for their impacts on the dynamic comb displacement and driving voltage. Under the condition of a driving DC bias voltage of 100 volts, the ECA displacement is firstly increased and then gradually decreased by increasing the comb finger length. The maximum displacement of ECA with trapezoid comb is 47.2 μm while that of ECA with rectangle comb is 22.4 μm. By using trapezoid comb shape to replace the rectangle comb under the same conditions, the ECA displacement is improved 2-fold. To enhance the stability of ECA system, the intersection angle of 90˚ of crab-leg flexure beams shows highest stability that is great better than the intersection angle of 45° and 60° of crab-leg flexure beams by increasing the driving different DC bias voltage to 100 volts on ECA. The stability of the ECA with the intersection angle of 90° of crab-leg flexure beams is enhanced 1.1-fold and 1.4-fold compared to that of 45° and 60° of crab-leg flexure beams, respectively. This design provides an optimized approach to the electrostatic comb actuator that can be used expansively in the gyroscope, motion sensor, position sensor, imaging sensor, optical sensor, and so on.

In the study of materials and macromolecules by first-principle methods, the bond order is a useful tool to represent molecules, bulk materials and interfaces in terms of simple chemical concepts. Despite the availability of several methods to compute the bond order, most applications have been limited to small systems because a high spatial resolution of the wave function and an all-electron representation of the electron density are typically required. Both limitations are critical for large-scale atomistic calculations, even within approximate density-functional theory (DFT) approaches. In this work we describe our methodology to quickly compute delocalization indices for all atomic pairs, while keeping the same representation of the wave function used in most compute-intensive DFT calculations on high-performance computing equipments. We describe our implementation into a post-processing tool, designed to work with Quantum ESPRESSO, a popular open-source DFT package. In this way we recover a description in terms of covalent bonds from a representation of wave function containing no explicit information about atomic types and positions.

Sentinel-1 satellite system continuously observes European countries in a relatively high revisit frequency of 6 days per orbital track. Given the Sentinel-1 configuration, most areas in Czechia are observed every 1–2 days by different tracks in a moderate resolution. This is attractive for various types of analyses by various research groups. The starting point for processing is an original data provided by ESA, for interferometry (InSAR) this level is a Single Look Complex (SLC) data. This work represents advantages of storing data augmented to a specifically corrected level of data, SLC-C. The presented database contains Czech nation-wide Sentinel-1 data stored in burst units that have been preprocessed to the state of a consistent well-coregistered dataset of SLC-C. These are resampled SLC data with their phase values reduced by a topographic phase signature, ready for fast interferometric analyses (an interferogram is generated by a complex conjugate between two stored SLC-C files). The data can be used directly into multitemporal interferometry techniques, e.g. Persistent Scatterers (PS) or Small Baseline (SB) techniques applied here. A further development of the nation-wide system utilising SLC-C data would lead into a dynamic state where every new pre-processed burst triggers a processing update to detect unexpected changes from InSAR time series and therefore provide a signal for early warning against a potential dangerous displacement, e.g. a landslide, instability of an engineering structure or a formation of a sinkhole. An update of the processing chain would also allow use of cross-polarised Sentinel-1 data, needed for polarimetric analyses. The current system is running at a national supercomputing centre IT4Innovations in interconnection to the Czech Copernicus Collaborative Ground Segment (CESNET), providing fast on-demand InSAR results over Czech territories. A full nation-wide PS processing using data over Czechia has been performed in 2017, discovering several areas of land deformation. Its downsampled version and basic findings are demonstrated within the article.

China is facing the development requirements of the dual carbon goal and the construction needs of resilient cities. Currently, high-strength composite structural systems are a feasible solution, but their research and application in the construction field are insufficient, especially for the combination forms of concrete above C100 and steel above Q550. In response to this issue, this paper proposes a new high-performance structural system, namely the composite frame - high-strength steel plate wall core tube resilient structure system, which includes high-strength steel plate (Q550) - ultra-high strength concrete (C100) shear walls, replaceable energy dissipation coupling beams, and composite frames. Taking a 200 meter building as an example, this paper designs and establishes calculation models for a conventional reinforced concrete frame-core tube structure and a high-performance structure based on PKPM software, and establishes elastoplastic analysis models for both based on SAUSAGE software, and then conducts dynamic elastoplastic time history analysis and seismic resilience assessment of structures under design basis earthquakes (DBEs), maximum considered earthquake (MCEs) and extremely rare earthquakes (EREs). Research has shown that compared to conventional structures, new high-performance structures can effectively reduce the size of shear walls, reduce the self-weight of the structure, and enhance the space for building use; The story drift ratio has higher redundancy than specification limits, lower plastic damage and overall stiffness degradation of the structure, and better seismic performance; The seismic resilience has significantly improved, especially in the case of casualties, which can better ensure the safety of people's lives and property.

This research investigates the fatigue behaviour and fracture mechanics of high-performance concrete (HPC), including various compositions such as HPC with basalt aggregates (HPC-B), HPC with gravel (HPC-G), and high-strength coarse mortar (CM) static and cyclic tensile loading within the special priority program SPP 2020. The study aims to integrate fracture mechanics into structural analysis to enhance design guidelines for slender cross-sections and safety-related high-performance structural components. The experimental investigations reveal HPC-B's re-markable superiority, displaying higher compressive strength, modulus of elasticity, and tensile strength compared to HPC-G and CM. A modified disk-shaped compact tension (MDCT) based on ASTM standards, aided by digital image correlation (DIC) unveils fracture behaviour, empha-sizing fracture energy as a crucial parameter. HPC-B exhibits improved crack resistance and notch sensitivity reduction attributed to crushed basalt aggregates and enhanced interfacial transition zone (ITZ). The research scrutinizes factors like material characterization, aggregate morphology, stress levels, and displacement rate on crack formation. High-cycle fatigue tests show HPC-B's superior performance, and post-fatigue analysis reveals enhanced residual fracture toughness attributed to nano-level structural changes, stress redistribution and aggregate-matrix interac-tion. 3D image analysis via Computed Tomography (CT) scans captures mesostructural crack propagation and provide quantitative insights. This study advances the understanding of HPC fatigue behaviour, emphasizing the interplay of aggregate types, morphologies, and their dy-namic response to cyclic loading, offering valuable insights for optimizing design guidelines and fostering innovation in structural engineering.

UNESCO highlights socio-emotional, cognitive, and behavioral learning as critical for advancing sustainability. As major events, sports generate significant social, economic, and environmental impacts. This study analyzed how academic background, gender, age, sport type, and modality influenced elite Colombian Olympic and Paralympic athletes' socio-emotional competencies. Methods: Colombian elite athletes who were members of the “Support to the Excellence Coldeportes Athlete” participated in the study. The total population studied included 334 Colombian elite athletes: mean age 27.10 ± 6.57 years old with 13.66 ± 6.37 years practicing his/her sports modality. The socio-emotional competencies of the participants were assessed using the Socio-emotional Competencies Scale (SECS). Results: Men scored higher in interpersonal regulation, self-awareness, conflict resolution, and emotional regulation. Younger athletes (≤25 years) had higher self-consciousness. Individual versus team sports did not differ. Paralympians showed higher motivation, self-awareness, teamwork, and emotional regulation. University educated athletes scored higher in motivation, self-awareness, teamwork, and empathy. All competencies positively correlated. Conclusion: Individualized interventions targeting improved socio-emotional performance are vital for sustainability and should account for differences across gender, age, education, and disability status.

Grouting materials with good insulation and reinforcement performance are key factors in solving the temperature control problem of high temperature tunnels using curtain grouting, while existing grouting materials cannot balance their working performance and insulation performance in high-temperature environments. In response to the above issues, this article uses red mud to prepare high ground temperature red mud based grouting materials (RMGS), and conducts tests on the working performance (viscosity, setting time, compressive strength) and insulation performance (thermal conductivity, specific heat capacity) of grouting materials at different temperatures (20, 40, 60, and 80 ℃), and analyzes the variation patterns and micro mechanisms of various characteristics with temperature. The results indicate that an increase in temperature will accelerate the viscosity development and condensation of the grouting material, and lead to a decrease in thermal conductivity and specific heat capacity. In addition, an appropriate temperature can also improve the compressive strength of the material. The increase in temperature will accelerate the hydration reaction rate of the grouting material and the development of internal pores, which is the reason why temperature affects the performance of the grouting material. In engineering applications, cement slurry is suitable for static water grouting, cement-sodium silicate slurry is suitable for dynamic water grouting, and RMGS is suitable for high-temperature tunnel grouting.

This study investigates the nonlinearities in three-phase inverters for SiC-based systems and compares their performance to IGBT-based systems. An analytical model of inverter voltage distortion is developed, which accounts not only for dead time (td), switching delay time, switching frequency (fs), and voltage drops of power devices, but also for output parasitic capacitance (Cout). Experimental tests validate the model, which provides a more accurate estimate of the inverter’s output phase voltage distortion. The power device characteristics are obtained from datasheets, while Cout is determined through experimentation. Three-phase inverters with varying switching frequencies, fundamental frequencies, and dead-time values are used in simulations and experiments to determine the influence of nonlinearity on phase voltage deviation and current distortion. The results show that, due to SiC devices’ faster switching time, the phase voltage deviation and phase current distortion are lower in SiC-based inverters than in IGBT-based ones for high-frequency applications, as the dead time can be reduced.

This paper introduces an advanced method that integrates contingent valuation and machine learning (CVML) to estimate residents’ demand for reducing or mitigating environmental pollutions and climate change. To be precise, CVML is an innovative hybrid machine-learning model, and it can leverage a limited amount of survey data for prediction and data enrichment purposes. The model comprises of two interconnected modules: Module I, an unsupervised learning algorithm, and Module II, a supervised learning algorithm. Module I is responsible for grouping the data into groups based on common characteristics, thereby grouping the corresponding dependent variable, whereas Module II is in charge of demonstrating the ability to predict and the capacity to appropriately assign new samples to their respective category based on input attributes. Take a survey on the topic of air pollution in Hanoi in 2019 as an example, we found that CVML can predict households’ willingness– to– pay for polluted air mitigation at a high degree of accuracy (i.e., 98%). We found that CVML can help users reduce costs or save resources because it makes use of secondary data that is available on many open data sources. These findings suggest that CVML is a sound and practical method that could be widely applied in a wide range of fields, particularly environmental economics and sustainability science. In practice, CVML could be used to support decision-makers in improving the financial resources to maintain and/or further support many environmental programs in years to come.

The durability of bridge decks is a critical factor in ensuring the safety and longevity of transportation infrastructure. This article explores advanced materials and innovative techniques designed to mitigate cracking in bridge decks, a common issue that can compromise structural integrity and increase maintenance costs. It provides a comprehensive review of cutting-edge solutions such as Fiber-Reinforced Polymers (FRP), High-Performance Concrete (HPC), Ultra-High Performance Concrete (UHPC), and self-healing concrete technologies. Each material is analyzed for its ability to enhance strength, reduce permeability, and improve resistance to environmental stressors. Additionally, the article examines the role of surface treatments, sealants, and routine maintenance practices in prolonging the life of bridge decks. By synthesizing current research and practical applications, this article offers valuable insights and recommendations for engineers, contractors, and policymakers seeking to advance bridge deck durability through innovative materials and methodologies.

The article presents a method of generating Key Performance Indicators related to electric motor energy efficiency on the basis of Big Data gathered and processed in frequency converter. The authors proved that using the proposed solution it is possible to specify the relation between the control mode of an electric drive and the control quality-energy consumption ratio in the start-up phase as well as in the steady operation with various mechanical loads. The tests were carried out on a stand equipped with two electric motors (one driving, the other used to apply the load by adjusting the parameters of the built-in brake). The measurements were made in two load cases, for motor control modes available in industrially applied frequency converters (scalar V/f, vector Voltage Flux Control without encoder, vector Voltage Flux Control with encoder, vector Current Flux Control and Vector Current Flux Control with torque control). During the experiments values of current intensities (active and output), the actual frequency value, IxT utilization factor, relative torque and the current rotational speed were measured and processed. Based on the data the level of the energy efficiency was determined for various control modes.

This study investigated the aerodynamic performances of various trains with different nose shapes, using as design variables two angles α,β for the heads shape and the bluntness angle γ, without crosswind. The effect on aerodynamic performances, such as the train drag coefficient, pressure distribution along the train surface, flow structures around the train and the wake, head pressure pulse, were analyzed. The results indicate that the increase of the train nose length for flat shapes decreases the CD values by 21.47% and 19.11%, decreasing the high-pressure region in the leading head. The duck-nose configuration emerges as a compromise between drag reduction and nose length. Increasing the angle γ a further drag reduction of 8.5% is featured. Drag formation along the train is also analyzed. The steeper the variation of the geometry, the higher the peak intensity and the slope of the curve. Regarding the flow features around the train, two-main counter-rotating vortices are captured in the wake. Moreover, the higher the nose length and the higher the bluntness angle γ, the weaker and narrower the wake. Again, a longer nose shape yields to a softer jump in terms of pressure difference, crucial for train homologation and safety.

The objective of this study was to investigate the relationship between high-performance work systems and a company’s social performance, taking into account the organizational climate as a situational variable. Specifically, this study aimed to analyze the impact of perceptions of a high-performance work system on the perception of a company’s social responsibility (CSR) among employees of Jordanian companies with an organizational size of 300 or more. This study used 175 valid questionnaires, and SPSS and AMOS 24 were used to test the hypotheses. The results showed that there was a positive relationship between the perception of a high-performance work system and the perception of a company’s CSR. Furthermore, this study found that a positive diversity climate could enhance the positive relationship between the two. These findings suggest that high-performance work systems can contribute to the development of sustainable human resource management systems and that a positive diversity climate is essential in shaping these systems. Based on these results, it is recommended that companies implement HPWSs and encourage workforce diversity to maximize the value of CSR activities and ensure their sustainability.

The incorporation of polycarboxylate ether superplasticizer (PCE) type polymer and silica fume (SF) in high-performance concretes (HPC) leads to the achievement of remarkable rheological and mechanical improvements. In the fresh state, PCEs are adsorbed on cement particles and dispersants, in turn promoting the workability of the concrete. Silica fume enables a very well compacted concrete to be obtained, which is characterized by high mechanical parameters in its hardened state. Some PCEs are incompatible with silica fume, which can result in the loss of slump and in poor rheological behavior. The main objective of the research is the study of the influence of three types of PCEs, which all have a different molecular architecture, on the rheological and mechanical behavior of high-performance concretes containing 10% of SF as a partial replacement of cement. The results show that the carboxylic density of PCE has an influence on its compatibility with SF.

This paper seeks to optimize the mechanical and durability properties of ultra-high performance concrete (UHPC). To meet this objective, concrete specimens were manufactured by using 1,100 kg/m3 of binder, water/binder ratio 0.20, silica sand and last generation of superplasticizer. Silica fume, metakaolin and two types of nano silica were used for improving the performances of the concrete. Additional mixtures included 13mm long OL steel fibers. Compressive strength, electrical resistivity, mercury intrusion porosimetry tests and differential and thermogravimetric thermal analysis were carried out. The binary combination of nano silica and metakaolin, and the ternary combination of nano silica with metakaolin and silica fume, led to the best performances of the UHPC, both mechanical and durable performances.

A novel polyphenol‑coated CoFe₂O₄ system was synthesized as a magnetic adsorbent by chemical oxidative polymerization process for magnetic solid-phase extraction of lawsone. The synthesized nanoadsorbent showed a spherical morphology with diameters under 50 nm by scanning electron microscopy images. The extraction efficiency of this adsorbent was studied towards the extraction of lawsone from saline aqueous solution in dispersion mode. Major parameters including the type and volume of desorption solvent, amount of sorbent, desorption time, extraction time, extraction temperature, ionic strength and pH were optimized. Under the optimum conditions the relative standard deviation in 0.005 µg mL^-1 (inter-day n = 6; intra-day: n = 6; and adsorbent to adsorbent n = 4) were obtained as 5.2, 8.07 and 11.7%, respectively. A linear calibration curve in the range of 0.003–0.5 µg mL^-1 with R² = 0.993 was obtained. The limit of detection and limit of quantification of the method were 0.001 µg mL^-1 and 0.003 µg mL^-1, respectively. The relative recovery percentages were in range of 90-96.4% for henna leaves, henna shampoo, and henna dermal lotion real samples.

In order to apply RAP fine aggregate to High Modulus Asphalt Concrete(HMAC), this study first extracts aged asphalt from RAP fine aggregate through extraction and distillation, and adds it to high modulus modified asphalt in proportions of 20%, 40%, 60%, and 80%. The performance changes of the recycled asphalt were investigated using conventional performance tests and dynamic shear rheological tests (DSR). Microscopic techniques such as four‐component analysis and infrared spectroscopy were employed to explain macroscopic performance changes in terms of component and functional group changes in the asphalt. Using HMAC‐20 gradation as an example, uniaxial compression dynamic modulus tests, rutting tests, freeze‐thaw splitting tests, and low‐temperature bending tests were conducted to evaluate the performance changes of recycled asphalt mixture with different RAP fine aggregate contents through four indices: dynamic modulus, dynamic stability, tensile strength ratio (TSR), and maximum bending strain. The results indicate that for every 20% increase in aged asphalt, the penetration at 25°C, ductility at 25°C, and dynamic viscosity at 60°C decrease by approximately 6.2%, 15.4%, and 3.9%, respectively, while the softening point increases by approximately 13.3%. It is concluded that the addition of aged asphalt significantly affects the ductility at 25°C and the softening point. As the aged content increases, the complex shear modulus (G*) and phase angle (δ) of the recycled asphalt decrease, while the rutting factor increases. The four‐component analysis and infrared spectroscopy of the asphalt demonstrate that the incorporation of aged asphalt is merely a physical blending phenomenon, involving changes in the content of the four components without the formation of new functional groups (new peaks). When RAP fine aggregate is applied to high modulus recycled asphalt mixture, compared to high modulus modified asphalt mixture, the recycled mixtures exhibit higher dynamic modulus and dynamic stability, but significantly reduced water stability and low‐temperature performance.

Bitumen is produced from non-renewable natural resources, continuously deplete and intrigues researchers to look for alternative binders. Annually, tons of waste engine oil (WEO) and crumb rubber (CR) are discarded unsustainably and pose a significant environmental threat. Adding these industrial waste products to asphalt provides a safe and cost-effective way for their disposal and improves the bitumen's performance in parallel. This study uses various combinations of waste engine oil and crumb rubber with 60/70 penetration grade bitumen to produce a partially synthetic bitumen. Adhesion being one of the critical characteristics of the bituminous binder has been assessed using the bitumen bond strength test along with physical and rheological properties. Results showed that waste engine oil with crumb rubber inclusion increases penetration and decreases the softening point, viscosity, complex modulus, and bonding strength. Additionally, polyphosphoric acid (PPA) in smaller dosages was also incorporated into the optimum percentage of CR + WEO to improve the properties of the binder. The results confirm that binder modification with waste engine oil and crumb rubber can be more effective with PPA. It is concluded that 35% of waste can replace the virgin binder giving a cost-effective and environmentally friendly solution.

The electron Transport layer (ETL) plays a crucial role in solar cell technology, particularly in perovskite solar cells (PSCs), where nanostructured TiO2 films have been investigated as superior ETL’s compared to compact TiO2. In this study we explore the nanocolumnal growth of TiO2 in the anatase phase, for bilayer thin films by magnetron sputtering (MS) technique and glancing angle deposition (GLAD). By optimizing the growth parameters, we achieved the formation of TiO2 nanocolumns with a cross-sectional diameter ranging from 50 to 75 nm. The average thickness of the films exceeded 12.71 ± 0.5µm , while the filaments exhibited an inclination angle close to 70◦ , corresponding to the deposition angle. Furthermore, we observed a correlation between the quality of the initial layer and enhanced growth of the TiO2 nanocolumns.

The accumulation of steel slag and other industrial solid wastes has caused serious environmental pollution and resource waste. The resource utilization of steel slag is imminent. In this paper, alkali activated materials ultra-high performance concrete (AAM-UHPC) was prepared by replacing GGBFS powder with different proportions of steel slag powder, and its workability, mechanical properties, curing condition, microstructure, and pore structure were investigated. The results illustrate that the incorporation of steel slag powder can significantly delay the setting time and improve the flowability of AAM-UHPC, making it possible for engineering applications. The mechanical properties of AAM-UHPC showed a tendency to increase and then decrease with the increase of steel slag dosing, and reached the best performance at 30% dosage of steel slag. The maximum compressive strength and flexural strength are 157.1 MPa and 16.32 MPa, respectively. High temperature steam or hot water curing at early age was beneficial to the strength development of AAM-UHPC, but continuous high temperature hot and humid curing would lead to strength inversion. When the dosage of steel slag is 30%, the average pore diameter of the matrix is only 8.43nm, and the appropriate steel slag dosage can reduce the heat of hydration and refine the pore size distribution, making the matrix more dense.

Low learning rates in Convolutional Neural Networks (CNNs) for image segmentation tasks can lead to convergence issues, unstable models, large oscillations, risk of divergence, sensitivity to the initial weights, and biases of the model. All these challenges make CNNs computationally expensive and require considerable training data. In contrast, achieving a high learning rate has been a significant challenge for CNNs. In response to the abovementioned challenge, this work aims to increase the learning rate of the image segmentation process. Images of any object in the universe taken from any camera can broadly be divided into spectral and geometric properties. Both these properties are prominently visible in satellite images. Therefore, if a method can classify satellite images, it can be applied to classify any image using the same technique. In this paper, Evolutionary Convergent Functions (ECF) are proposed. They convert features' spectral and geometric properties in a satellite image into mathematical equations using a decision tree and then converge with a neural network. Different high-resolution data have been chosen to extract additional features from it. This transformation process, anchored in decision tree methodology, converges with neural networks to yield unparalleled results, all while eliminating the need for computationally intensive convolutions. The proposed method extends beyond conventional boundaries by using varied high-resolution datasets. Each dataset is carefully selected with distinctive features. This research explores the untapped potential of ECF to advance the field of automated image classification, aiming to broaden the scope of current methodologies. The synergy between spectral and geometric properties emerges as a powerful combination, endowing our methods with the ability to extract nuanced and context-rich information for image analysis. The results show a high accuracy, i.e., above 90%, in almost all objects of different shapes and spectral signatures. Additionally, in terms of prediction time, ECF is faster than U-Net, a state-of-the-art method that is evidence of ECF efficiency and speed.

The drug discovery process is a complex and time-consuming endeavor that can greatly benefit from advancements in computational biology. This study explores the implementation of GPU-accelerated computational methods to enhance molecular docking simulations and virtual screening techniques, two crucial components of drug discovery. By leveraging the parallel processing capabilities of Graphics Processing Units (GPUs), we significantly accelerate the analysis of protein-ligand interactions and the identification of potential lead compounds. Our approach utilizes optimized algorithms and software frameworks to achieve substantial speedups over traditional CPU-based methods. Results demonstrate that GPU-accelerated molecular docking simulations and virtual screening can reduce computation times by up to 10-fold, enabling rapid and accurate identification of promising drug candidates. This accelerated workflow has the potential to revolutionize the drug discovery pipeline, facilitating the development of novel therapeutics and reducing the time and cost associated with bringing new treatments to market.

Although Sugarcane bagasse ash (SCBA) has good cementitious property, previous researchers have primarily aimed to improve the mechanical performance of conventional concrete or cement-based composites. Research is lacking on ultra-high performance concrete (UHPC), especially regarding tensile self-sensing properties. This paper aimed to comprehensively evaluate the SCBA effect on the UHPC’s mechanical, electrical, and tensile self-sensing properties. The results showed that SCBA below the critical incorporation concentration (CIC) improved the UHPC’s mechanical properties compared to steel fibers alone. Furthermore, adding 3.0wt% SCBA remarkably enhanced the UHPC mechanical properties where the compressive, flexural, and tensile strengths increased by 13.1%, 17.4%, and 20.6%, respectively. The SCBA content of 0.3wt% achieved the maximum UHPC electrical resistivity since values smaller or greater than this content decreased the resistivity. The SCBA-enhanced UHPC showed better tensile stress-sensing performance than UHPC without SCBA due to improved linearity and reversibility, lower hysteresis, higher sensitivity, and superior repeatability. UHPC with 0.3wt% SCBA achieved the best linearity, while UHPC with 0.9wt% SCBA showed the highest sensitivity, lowest hysteresis, and best repeatability. Overall, the content of 0.9wt% SCBA is the best in improving the overall mechanical, electrical, and tensile self-sensing performance.

This experimental study examined the application effect of polycaprolactone (PCL), an organic resin material with excellent elasticity and ductility, on improving the mechanical performance of cellulose nanocrystal (CNC) cement composites. To analyze the effect, a test specimen containing CNC and PCL mixed, containing only CNC, and a control (plain) specimen containing nothing were compared. PCL was compared according to its shape, and in the case of Granules, which is the basic shape, interfacial adhesion with cement was not achieved, so a DCM solution was used to dissolve and use the Granules form. As a method for bonding PCL to the CNC surface, the CNC surface was modified using 3-aminopropyltriethoxysilane (APTES), and surface silylation was confirmed through Fourier transform infrared spectroscopy(FT-IR) analysis. To evaluate the dispersibility according to the application of PCL to the modified CNC, particle size analysis (PSA) and zeta potential analysis were performed according to the PCL mixing amount (0.1, 0.3, 0.5, and 1 vol.% compared with those of CNC) and size distribution. Through the zeta potential value, the highest dispersion stability was shown at 1 vol.%. The cohesive force of CNC was low and the dispersion stability was high according to the application of PCL. According to the results of the dispersion stability evaluation, the degree of hydration of the dissolved PCL 1 vol.%, CNC-only specimens, and plain specimens were analyzed. Hydration products increased by increasing the degree of hydration in the unhydrated area. However, the incorporation of PCL showed a low degree of hydration, and the analysis of strength characteristics also showed a decrease of approximately 27% compared with that of plain specimens. This was because the bonding with SiO2 was not smooth owing to the solvent, thus affecting internal hydration. To investigate the effect of the PCL shape, the compressive and flexural strength characteristics were compared using PCL powder as an additional parameter. The compressive and flexural strength values were improved by approximately 54% and 26%, respectively, compared with those of the plain specimen. Scanning electron microscopy (SEM) analysis determined that the filler effect that made the micropore structure denser, affected the mechanical performance improvement.

Two reversible furan-maleimide resins, in which there are rigid -Ph-CH2-Ph- structure and flexible -(CH2)6- structures in bismaleimides, were synthesized from furfuryl glycidyl ethers (FGE), 4,4′-diaminodiphenyl ether (ODA), N,N'-4,4'-diphenylmethane-bismaleimide (DBMI) and N,N'-hexamethylene-bismaleimide (HBMI). The structures of the resins were confirmed by Fourier transform infrared analysis, and the thermoreversibility was evidenced by differential scanning calorimetry (DSC) analysis as well as sol-gel transformation process. Mechanical properties and recyclability of the resins were preliminarily evaluated by the flexural test. The results show the Diels-Alder (DA) reaction occurs at about 90°C and reversible DA reaction does at 130-140°C for the furan-maleimide resin. Thermally reversible furan-maleimide resins have high mechanical properties. The flexural strength of cured FGE-ODA-HBMI resin arrives at 141 MPa. The resins have a repair efficiency of over 75%. After being hot-pressed for three times, two resins display higher flexural strength than 80 MPa.

This paper describes the packaging models that are fundamental for the design of ultra-high-performance concrete (UHPC), and their evolution. They are divided into two large groups: continuous and discrete models. The latter are those that provide the best answer in obtaining an adequate simulation of the packing of the particles up to nanometric size. This includes the interaction among the particles by means of loosening and wall coefficients, allowing a simulation of the virtual and real compactness of such particles. In addition, a relationship between virtual and real compactness is obtained, through the compaction index, which may simulate the energy of compaction that the particles undergo in their placement in the mold. The use of last-generation additives allows such models to be implemented with water-cement (w/c) ratios close to 0.18. However, the premise of maximum packing as a basic pillar for the production of UHPC should not be the only one. The cement hydration process affected by nanoadditions and the ensuing effectiveness in the properties in both fresh and hardened state according to the respective percentages in the mixture should also be studied. An adequate ratio and proportion of these additions may lead to an obtaining of better results even with lower levels of compactness.

This paper presents the systematic development and performance characterization of a non-proprietary 3D-printable ultra-high-performance fiber-reinforced concrete (UHPFRC) for digital construction. Several fresh and hardened properties of the developed 3D-printable UHPFRC matrix (without fiber) and composite (with 2% volume fraction of steel fibers) were evaluated and compared to that of conventionally mold-cast UHPFRC. Additionally, the effects of testing direction on the compressive and flexural strengths of the printed UHPFRC were investigated. The fresh properties of the UHPFRC developed in this study satisfied the criteria for extrudability, buildability, and shape-retention-ability, which are relevant for ensuring printability. The printed UHPFRC exhibited superior flexural performance to the mold-cast UHPFRC due to alignment of the short fibers in the printing direction. The high compressive and flexural strengths, along with the deflection-hardening behavior, of the developed UHPFRC can enable the production of thin 3D-printed components with significant reduction or complete elimination of conventional steel bars.

The purpose of this study is to investigate the moderating effect of technology configuration capability on the relationship between strategic flexibility and organizational performance through different stages of technological life cycle. Through the empirical research on the 439 Chinese high-tech organizations, it shows that a technological configuration capability has enhanced the effect of strategic flexibility to the organizational performance in the complex dynamic environment. However, the impact on the different stages of technological life cycle is different. In addition, this paper explored strategic flexibility on different stages of technological life cycle on the basis of empirical study.

Due to the extraordinary mechanical properties of ultra-high-performance concrete (UHPC), the shear stirrups in UHPC beams could potentially be removed. The aim of this study is to determine the effect of beam height and steel fiber volume content on the shear behavior of non-stirrup UHPC beams under a larger shear span-depth ratio (up to 2.8). Eight beams were designed and fabricated, including six non-stirrup UHPC beams and two comparing stirrup-reinforced normal concrete (NC) beams. The experimental results demonstrated that the volume content of steel fiber could be a crucial factor affecting the ductility, cracking strength, and shear strength of non-stirrup UHPC beams and altering their failure modes. Additionally, the height of the beam had a considerable effect on its shear resistance. This study revealed that the French standard formulae were more accurate for the UHPC beams with larger shear span-depth ratios and PCI-2021 formulae greatly overestimate the shear capacity of UHPC beams with larger shear span-depth ratios. Xu’s formulae were more accurate for the steel fiber-reinforced UHPC beams with larger shear span-depth ratios.

High Vacuum Flat Plate Collectors (HVFPCs) are the only type of Flat Plate Thermal Collectors capable of producing thermal energy for middle-temperature applications (until 180 °C). As the trend of research plans to develop new Selective Solar Absorbers to extend the range of HVFPC application until 250 °C, it is necessary to correctly evaluate the collector efficiency up to such temperature to predict the energy production accurately. We propose an efficiency model for these collectors based on the selective absorber optical properties. The proposed efficiency model explicitly includes the radiative heat exchange with the ambient, which is the main source of thermal losses for evacuated collectors at high temperatures. It also decouples the radiative losses that depend on the optical properties of the absorber adopted from the other thermal losses due to HVFPC architecture. The model has been validated by applying it to MT-Power HVFPC manufactured by TVP-Solar, and it has been used to predict the efficiency and energy production of HVFPC equipped with new, optimized selective solar absorbers developed in recent years.

To determine promethazine (PMZ) and its metabolites promethazine sulfoxide (PMZSO) and monodesmethyl-promethazine (Nor1PMZ) in swine muscle, fat, kidney, and liver, a sample preparation and high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis method were established and validated. The sample was extracted with 0.1% formic acid-acetonitrile, and the extract was purified with acetonitrile-saturated n-hexane. After concentration by rotary evaporation, the extract was re-dissolved in 0.1% formic acid-water and acetonitrile (80:20, v/v). The HPLC-MS/MS column used was a Waters Symmetry C18 (100 mm × 2.1 mm i.d., 3.5 μm), with 0.1% formic acid-water and acetonitrile as the mobile phase, and the target compounds were determined by positive ion scan and multiple reaction monitoring. PMZ and Nor1PMZ were quantified with deuterated promethazine (PMZ-d6) as the internal standard, while PMZSO was quantified by external standard method. The limits of detection (LOD) and limits of quantification (LOQ) of PMZ and PMZSO in muscle, liver, and kidney spiked samples were 0.05 μg/kg and 0.1 μg/kg, respectively, while those of Nor1PMZ were 0.1 μg/kg and 0.5 μg/kg, respectively. The LOD and LOQ of analytes in fat spiked sample were 0.05 μg/kg and 0.1 μg/kg, respectively. The sensitivity of this method reaches or exceeds its presented in previous reports. The analytes PMZ and PMZSO showed good linearity within the range of 0.1 μg/kg to 50 μg/kg, while Nor1PMZ showed good linearity within the range of 0.5 μg/kg to 50 μg/kg, with correlation coefficients (r) greater than 0.99. The recoveries of the target compounds in the sam-ples were between 81.73% and 107.17%, and the precision ranged from 1.78% to 10.47%. This study developed for the first time an HPLC-MS/MS method for the determination of PMZ and its metabolites PMZSO and Nor1PMZ in four swine edible tissues, comprehensively covered target tissues of monitoring object, which is applicable for monitoring veterinary drug residues in ani-mal-derived foods and ensuring food safety.

Lipophilicity can be measured with different methods, such as Shake-Flask or liquid chromatography. HPLC presents the advantage of overcoming solubility issues and therefore extending the range of lipophilicity to high values. A specific HPLC method, called ELogD, had been developed 20 years ago on a C₁₆-amide stationary phase, enhancing hydrophobic and hydrogen bond interactions to mimic octanol-water partition. The emergence of novel stationary phases added to the need for a less complex mobile phase has led to the development of a new HPLC assay called alphaLogD, applicable to neutral and basic compounds at pH 7.4, that combines superficially porous particles, a high number of equilibriums between solutes and stationary phase, and results in a lower number of isocratic methods to determine the logk’_w at a higher throughput. Statistical studies have been run to successfully evaluate the alphaLogD method compared to the Shake-Flask method and to allow this lipophilicity measurement into the so-called Beyond-Rule-of-5-molecules space.

Predictive simulation of concurrent debris flow using only pre-disaster information has proven to be difficult as a result of problems in predicting the location of debris-flow initiation (i.e., slope failure). However, because catchment topography has concave characteristics, with all channels in a catchment joining each other as they flow downstream, it is possible to predict damage to downstream area using relatively inaccurate initiation points. Based on this, this paper presents methodologies employing debris-flow initiation points generated randomly using statistical slope failure prediction. A many-case simulation across numerous initiation points was performed to quantify the effect of slope-failure location in terms of deviations in the predicted water level and terrain deformation. It was found that the relative standard deviation diminished as the points approached the downstream area, indicating a location-based predictability effect.

Recently, research on ultra-high-performance concrete (UHPC) with the characteristics of ultra-high strength, high toughness, and high durability has been actively conducted from materials to actual structural applications. However, research on experimental methods that reflect the unique characteristics of UHPC, which are different from those of normal concrete, is insufficient. To solve the difficulties in applying the compressive creep test method for normal concrete to UHPC and to verify the validity of the recently proposed ASTM C 1856 standard test method, this study performed compressive creep tests on UHPC with variables of specimen size, sustained load intensity, fiber length, and strain measuring method. Two types of cylindrical specimens of ϕ100 × 200 mm and ϕ75 × 150 mm were used for a specimen size variable. The sustained load intensities of 10% to 40% based on the specified compressive strength of 150 MPa were applied. Two types of steel fiber lengths of 13 mm and 19.5 mm in the UHPC mix were used as a variable, and three strain measuring methods of the embedded strain gauge method, the attached strain gauge method, and the strain meter with stud measured the change in length of the UHPC specimens. The experimental results showed that the strain meter method using the standard rod had limitations due to the error in the verification process using the standard rod and the observation error of the measurer, and the sustained load intensity is preferable to 15% to 40% of the actual compressive strength. It was found that it is possible to perform a compressive creep test for UHPC using a specimen with a size of ϕ75 mm or more, and the fiber length did not affect the creep behavior of UHPC up to 19.5 mm. As a result of comparing the UHPC creep test results with the creep prediction model, the B3 model was found to most accurately predict the UHPC creep behavior. The creep coefficient pattern of the B3 model was very similar to the measured results, and well reflected the effects of the mixing ratio and compressive strength of UHPC.

Lattice and code cryptography can replace existing schemes such as Elliptic Curve Cryptography because of their resistance to quantum computers. In support of public key infrastructures, the distribution, validation and storage of the cryptographic keys is then more complex to handle longer keys. This paper describes practical ways to generate keys from physical unclonable functions, for both lattice and code based cryptography. Handshakes between client devices containing the PUFs and a server are used to select sets of addressable positions in the PUFs, from which streams of bits called seeds are generated on demand. The public and private cryptographic key pairs are computed from these seeds together with additional streams of random numbers. The method allows the server to independently validate the public key generated by the PUF, and act as a certificate authority in the network. Technologies such as High performance computing, and graphic processing units can further enhance security by preventing attackers to make this independent validation when only equipped with less powerful computers.

The purpose of this study is to analyse the feasibility of using waste from granite gang saws (GCW-GS) to manufacture ultra-high performance, steel-fibre reinforced concrete (UHPFRC). These machines cut granite blocks by abrasion using a steel blade and slurry containing fine steel grit. The waste generated by gang saws (GCW-GS) contains up to 15% Fe2O3 and up to 5% CaO. This is the main difference from the waste produced by diamond saws (GCW-D). Consequently, the second objective of this study is to compare the results of the waste obtained with gang saws with that from diamond saws, in order to determine the influence of iron and calcium oxides. The waste from cutting granite with gang saws was used in different percentages to replace micronized quartz powder of natural origin in the manufacture of UHPRFC. All the test specimens were analysed to determine their compressive strength, elasticity modulus, flexural strength and indirect tensile strength. The final conclusion is that wastes from both gang saws and diamond saws can be used to manufacture UHPFRC with an improvement in the mechanical properties up to a 35% replacement. The results for GCW-GS are better, mainly due to the pozzolanic effect of the iron dioxide. For higher percentage replacements the mechanical properties are close to the control concrete with small decreases.

This study aims to suggest a basis for the selection of technologies for developing high-performance buildings to reduce energy consumptions and greenhouse gas emissions. Energy-saving technologies comprising of 15 cases were categorized into passive, active, and renewable energy systems. EnergyPlus v8.8 was used to analyze the contribution of each technology in reducing the primary energy consumptions and CO₂ emissions in the Korean climate. The primary energy consumptions of the base model were 464.1 and 485.1 kWh/m²a in the Incheon and Jeju, respectively, and the CO₂ emissions were 83.4 and 87.4 kgCO₂/m²a, respectively. Each technology (cases 1–15) provided different energy-saving contributions in the Korean climate depending on their characteristics. The heating, cooling, and other energy-saving contributions of each technology indicate that their saving rates can be used when selecting suitable technologies during the cooling and heating seasons. Case 15 (active chilled beam with dedicated outdoor air system + ground source heat pump) showed the highest energy saving rate. In case 15, the Incheon and Jeju models were reduced by 189.4 (59.2%) and 206.2 kWh/m²a (57.4%) compared to the base case, respectively, and the CO₂ emissions were reduced by up to 32.7 (60.8%) and 35.6 kgCO₂/m²a (59.3%), respectively.

Abstract：Objective: To compare the effects of fermentation on the chemical composition of Gastrodia Tuder Halimasch Powder, and to provide a basis for the processing, processing and clinical application of this medicinal material. Methods: UHPLC-Q-Orbitrap HRMS was used to identify compound structure by comparing excimer ions, characteristic fragment ions and reference substances, and referring to relevant literature and database search. The main differential components were screened by orthogonal partial least squares-discriminant analysis (OPLS-DA), and the differential components were quantitatively studied by high performance liquid chromatography, and the differences in the types and contents of chemical components before and after fermentation were compared. Results: A total of 77 compounds were identified or preliminarily derived after fermentation, including 21 terpenes, 18 organic acids, 9 flavonoids, 7 nucleosides, 6 amides, 4 amino acids, 2 pyrrolidone derivatives, 2 sterols, 2 alkaloids and 6 others.Through high performance liquid chromatography, it was found that the content of daidzein genistein and ergosterol was significantly increased after fermentation, and a new component ergosterol was produced. Compared with Gastrodia Tuder Halimasch Powder before fermentation, the content of genistin and other components was significantly reduced, and there may be a process of genistin conversion to genistein.Conclusion: The fermentation process will have a certain influence on the types and contents of the chemical components of Gastrodia Tuder Halimasch Powder, and ergosterol, genistein and .daidzein may be the main pharmacodynamic components, which can provide a useful reference for the clinical rational application, quality control and mechanism of action of Gastrodia Tuder Halimasch Powder.

Aquaculture is an important source of food for the growing global population. To meet the increasing demand for fish, intensive aquaculture methods are commonly used. However, these methods can lead to stress and disease in fish, which can result in significant economic losses. Gamma-aminobutyric acid (GABA) is a non-protein amino acid that is found in the brain and central nervous system of animals as inhibitory neurotransmitter. It has been shown to have a variety of physiological functions, including stress reduction and immune enhancement. This study investigated the effects of dietary supplementation of GABA on growth, serum biochemistry, innate immunity and disease resistance in juvenile olive flounder (Paralichthys olivaceus) challenged with Edwardsiella tarda under high stocking density. A control diet and three experimental diets were prepared, with 150 mg/kg (GABA150), 200 mg/kg (GABA200), and 250 mg/kg (GABA250) of GABA added to each diet, respectively. The actual GABA content of the experimental diets was analyzed and found to be 63.9 mg/kg (CON), 231.3 mg/kg (GABA150), 291.6 mg/kg (GABA200), and 323.9 mg/kg (GABA250). Each experimental diet was fed to olive flounder with an initial weight of 12.75 g ± 0.3 g in 40 L tanks at two stocking densities: normal density (20 fish/tank) and high density (40 fish/tank). The experiment was conducted in triplicate for each diet and stocking density. The water temperature was maintained at 17.5 ± 1℃ and the salinity at 33 ± 1 ppt. After 8 weeks of feeding trial, growth, feed utilization, whole-body proximate composition, blood analysis, non-specific immune response, and challenge test were performed. There were no significant difference in weight gain and specific growth rate among the GABA concentrations and density × GABA concentration interaction groups. However, the low-density groups showed significantly higher values than the high-density groups (P &lt; 0.05). There was no significant difference in feed efficiency and protein efficiency ratio among all groups. Moreover, there was no significant difference in the whole-body general composition analysis (P &gt; 0.05). In the hematological analysis, there was no significant difference in cortisol in terms of diet and density × GABA concentration interaction, but the high-density experimental group showed a significantly higher value. Blood GABA significantly increased in proportion to the addition amount regardless of the density group (P&lt;0.05). Superoxide dismutase activity in the non-specific immune response showed that the GABA-added experimental group had a significantly higher value than the control group, but there was no significant effect of the density densities or density × GABA concentration interaction (P&lt;0.05). Myeloperoxidase activities in fish fed the GABA200 and GABA250 diets showed significantly higher values, and a density × GABA concentration interaction effect was observed (P&lt;0.05). Lysozyme activity was significantly higher in the GABA150 group than in the CON, GABA200, and GABA250 groups (P &lt; 0.05). After 15 days of challenge test by intraperitoneal injection of Edwardsiella tarda, the cumulative survival rates of the GABA150, GABA200, and GABA250 groups were significantly higher than that of the CON group (P &lt; 0.05). The results suggested that the optimal dietary GABA level for juvenile olive flounder culture is 150 mg/kg, regardless of rearing density, to enhance growth, immune function and disease resistance.

Banking sector is generally taken out of sample while the sustainability performance, and the financial performance are compared with each other. The core aim of this study is to analyze the effect of the declarations made in the cope of sustainability reports on the financial performance in the banking sector. Seven banks were included in the study which were placed at least one time in BIST Sustainability Index in between 2010-2017 years. Environment, human resources, product liability and community involvement were determined as sustainability criteria and return on assets, return on equity and net interest margin were determined as financial performance criteria. Non-Parametric Statistic Tests and Panel Data Analysis were used for analysis and types, and the sizes of banks were selected as dummy variables. As a result, it is found that the declarations of sustainability reports have a significant effect only on return on assets and have no significant effect on return on equity and net interest margin. And also, when we analyzed the relationship of sustainability criteria and return on assets, we found that the declarations about environment and human resources have negative effects on return on assets.

This paper aims to demonstrate how the detection of communities for epidemiological networks can be used to reduce the dimensionality of optimal control problems. A range of planted partition stochastic blockmodels are generated, where the underlying communities in the model vary in detectability. A high-performance computing workflow is then used to simulate and estimate reduced model dynamics for the networks, which in turn is used to find optimal control solutions.

This study examines the effect of a corporate citizenship-oriented high performance work system (CC-oriented HPWS) and the interactive influence of CC-oriented HPWS and person-organization fit (PO fit) on creativity at the individual level. Our analysis of field data collected from 303 employees in two branch companies with a two-phase collection method provides empirical support for our theoretical model. The results indicate that high performance work system (HPWS) reflecting corporate citizenship (CC) correlates positively with individual creativity. In addition, the moderating effect of PO fit is significant in the relationship between CC-oriented HPWS and creativity at the individual level. On this basis, this paper discusses the theoretical and practical implications of these findings for future research.

Objective: Lipids are secreted into milk as bilayer-coated structures: milk lipid globules (MLGs). Adipophilin (ADRP) and perilipin 3 (TIP47) are associated with MLGs in human breast milk; however, the role of these proteins in milk lipid secretion is not fully understood. The aim of the study was to investigate levels of ADRP, TIP47 and total lipid content in human breast milk, their mutual correlations and dynamics during lactation. Research Methods & Procedures: Milk samples from 22 healthy lactating women (Caucasian, Central European) were collected at five time points during lactation (1–3, 12–14, 29–30, 88–90 and 178–180 days postpartum). Mass spectrometry-based method was used for quantification of ADRP and TIP47 in the samples. The gravimetric method was used to determine milk total lipid content. Results: We observed distinctive trends in ADRP, TIP47 levels and lipid content in human breast milk during the first 6 months of lactation. We also found a significant association between lipid content and ADRP, lipid content and TIP47, and ADRP and TIP47 concentrations in breast milk at all sampling points. Moreover, we derived an equation for estimating the mean lipid content of milk based on ADRP concentration in human breast milk. Conclusions: A mass spectrometry-based method was developed for quantifying ADRP and TIP47 in human breast milk. Strong mutual correlations were found between ADRP, TIP47 and total lipid content in human breast milk.

In this paper we introduce a computational model for the simulation of hypervelocity impact (HVI) phenomena which is based on the Discrete Element Method (DEM). Our paper constitutes the first application of DEM to the modeling and simulating of impact events for velocities beyond 5 kms⁻¹. We present here the results of a systematic numerical study on HVI of solids. For modeling the solids, we use discrete spherical particles that interact with each other via potentials. In our numerical investigations we are particularly interested in the dynamics of material fragmentation upon impact. We model a typical HVI experiment configuration where a sphere strikes a thin plate and investigate the properties of the resulting debris cloud. We provide a quantitative computational analysis of the resulting debris cloud caused by impact and a comprehensive parameter study by varying key parameters of our model. We compare our findings from the simulations with recent HVI experiments performed at our institute. Our findings are that the DEM method leads to very stable, energy–conserving simulations of HVI scenarios that map the experimental setup where a sphere strikes a thin plate at hypervelocity speed. Our chosen interaction model works particularly well in the velocity range where the local stresses caused by impact shock waves markedly exceed the ultimate material strength.

Sustainability experienced significant global momentum in recent decades. The emergence of the sustainability movement in the Financial Sector was prompted by the increasing global climate change resulting from environmental deterioration. Primarily associated with the Financial Sector, it has increasingly been recognised that Financial Institutions are significant contributors to global climate change, both via direct and indirect means. This has resulted in a heightened emphasis and demand from stakeholders for the implementation of Sustainability Principles on a worldwide scale. This study presents a methodology that utilises the various dimensions of Sustainable Finance through the concept of Triple Bottom Line to determine the factors influencing the Sustainability Performance of Financial Institutions. The suggested approach is grounded and supported by the current body of literature. The study presents a hypothesis to investigate the correlation between factors of Green Finance Practices and Sustainability Performance of Banking Sector. The suggested framework can be utilised by regulatory bodies and individual financial institution of a country to identify the elements that influence the Sustainability Performance of Banking Sector.

This study assessed the relationship between body posture displacements and cardiopulmonary function test (CPET) and skill related physical fitness tests. One hundred male (60%) and female collegiate athletes (22.2 ± 4yrs) with a normal body mass index (BMI up to 24.9) were assessed via the PostureScreen Mobile app to quantify postural displacements as head, thorax and pelvis rotations and translations. CPET and physical performance tests including the agility T-test, the vertical jump test, the stork static balance test (SSBT) and the dynamic Y-balance test (YBT) were performed. Spearman correlation (r) and p-values are reported. Postural parameters were found to have moderate-to-high associations with CPET and the agility test, moderate correlations with the vertical jump test and the SSBT (head and pelvic postures only), and weak correlations with the YBT. As postural parameters were more asymmetric, both CPET and performance skills scores were worse. For examples: 1) a medium positive correlation was found between cranio-vertebral angle (CVA) and vertical jump test (r = 0.54; p-value &lt; 0.001) and SSBT (r = 0.57; p-value &lt; 0.001), while a strong negative correlation was found between CVA and agility test (r = -0.86; p-value &lt; 0.001). 2) A strong positive correlation was found between CVA and oxygen uptake efficiency slope, load watts VO2 at VT, VO2 / kg, load watts at respiratory compensation point (RCP), (r = 0.65 and r = 0.71; p &lt; 0.001). Conversely, a significant negative correlation was found between CVA and VE / VO2 at VT (r = -0.61; p &lt; 0.001). Postural rotations and translations of the head, thorax and pelvis were statistically correlated with physical performance skills and CPET in young collegiate athletes. There were moderate-to-high associations with cardiopulmonary functions and agility tests, moderate correlations with the vertical jump test, and weak correlations with the YBT. Postural alignment may be important for optimal physical performance and optimal cardiopulmonary function. Further research is necessary to elucidate reasons for these correlations as found in our sample of young and healthy athletes.

Nitraria tangutorum Bobrov is a berry shrub with white flowers and red fruits, which grows in the deserts in the Tibetan Plateau, Mongolia, and western China. Its fruit N. tangutorum fruit (NTF) contains various bioactive compounds, with anti-fatigue, anti-inflammatory, and neuroprotective functions. However, the high saccharide content of NTF makes it unsuitable for diabetic patients. In this study, we fermented NTF to obtain N. tangutorum fermented juice (NTJ), and N. tangutorum fermented residue (NTR), which are suitable for diabetics to consume. We characterized the bioactive compounds in NTF, NTR, and NTJ, and found that fermentation increased the diversity of bioactive compounds, and greatly reduced sucrose, glucose, and fructose content while generating trehalose, which has the potential to manage blood glucose levels. Further, NTJ displays anti-diabetic potential due to various compounds anti-diabetic properties. This study provides a basis for further clinical research on NTJ's anti-diabetic function in humans.

The paper is dedicated to the topical problem of examining permafrost state and the processes of its geocryological changes by means of geophysical methods. To monitor the cryolithozone, we propose and scientifically substantiate a new technique of pulsed electromagnetic cross-well sounding. Based on the vector finite-element method, we created a mathematical model of the cross-well sounding process with a pulsed source in a three-dimensional spatially heterogeneous medium. A high-performance parallel computing algorithm was developed and verified. Through realistic geoelectric models of permafrost with a talik under a highway, constructed following the results of electrotomography field data interpretation, we numerically simulated the pulsed sounding on the computing resources of the Siberian Supercomputer Center of SB RAS. The simulation results suggest the proposed system of pulsed electromagnetic cross-well monitoring to be characterized by a high sensitivity to the presence and dimensions of the talik. The devised approach can be oriented to addressing a wide range of issues related to monitoring permafrost rocks under civil and industrial facilities, buildings and constructions.

There is a paucity of data on the progression of track cycling sprinters, and the evolution of training and performance over a training cycle. Following prior research showing relationships between sprint cycling power and endurance cycling power, we compared these relations over a 3-month period. Our hypothesis is large improvements in power would come from training either for sprint power or sprint capacity, and this would be reflected in the data. A total of publicly available 25 datasets. These data were plotted against the line of best fit for 30-s and 2-min power to assess if training towards the line led to better 30-s power. We observe the best performances came from riders who start below the 30-s vs 2-min power line of best fit, with greater capacity and ability to ride fast over a sprint series, and progressing towards focusing on specific power in the final block leading into a competition. These results support the hypothesis of combined capacity and power training based on rider-specific relationship to the line of best fit between these measures versus a strictly maximal power training focus.

In this paper, it is defined that how the IPOs are affected by the underwriter reputation. What will happen to IPOs who are backed by reputed underwriter and who is not? And evaluate the IPOs short-run and long-run performance. Also try to explain the relationship between IPOs and underwriter. Also tried to compare some of the international return and Pakistani returns. Also determine the relation of the investor to the underwriter. There is a set of investors who have there own relationship with the underwriters how they will affect on the IPOs in the market.

Background/Objectives: Potassium iodate and potassium iodide were commonly fortified in iodized table salt, where continuous monitoring is needed to maintain the quality. Our study reported an optimized detection method for total iodine in iodized table salt using 0.5 M sodium bisulfite as reducing agent; Methods: The iodized table salt (0.5 g) was dissolved in 0.5 M sodium bisulfite solution prior to injection in ultra high-performance liquid chromatography (UHPLC) coupled with diode array detector using weak anion-exchange column (2.1 mm×150 mm, 5 μm); Results: Iodide was eluted at 9.92±0.06 minutes (λ=223 nm) when isocratic mobile phase of 1:1 (v/v) methanol:120 mM phosphate buffer mixed with tetrasodium pyrophosphate (pH 3.0) was running at 0.20 mL/min (15 minutes). Iodide detected as total iodine from 10 to 60 mg/Kg with limit of detection (LOD) 1.2 mg/Kg and limit of quantification (LOQ) 3.7 mg/Kg. The method was validated with relative standard deviation (RSD) of 4.2%, 0.4%, 1.6%, 0.8% for accuracy, repeatability, intermediate-precision and robustness, respectively. The determination of total iodine was successful on six (6) samples (n=3), which recovered 87.2%-106.9% of iodate and iodide spike; Conclusions: Thus, the study provided a validated protocol for the determination of total iodine in iodized table salt using 0.5 M sodium bisulfite.

The composite post insulator composed of a large diameter mandrel and flange pressed by one-time integral pultrusion has high strength and good toughness, widely used in the existing flexible converter valve. For constructing the finite element model of the crimped post insulator, most of the flange surface and the mandrel surface are directly connected, making the simulation model's stiffness deviate from the actual model and the model distortion. Based on this, this study proposes a new finite element simplified model of crimped composite post insulator based on Multi-Point Constraints (MPC), which accurately simulates the crimping area of man-drel and flange in engineering practice. It verifies the model's reliability through static tests and characteristic vibration tests. The results show that the difference between the finite element simulation and test results is within 5 %. The finite element modeling method based on MPC local constraints is very reliable in applying crimped composite post insulators. Based on the proposed model, the influence of flange height and thickness on the mechanical properties of composite post-insulators is further explored. The results show that the influence of flange height on mechanical properties can be ignored when the height of the insulator stiff-ener ( non-press common area ) is constant. Within the allowable thickness range of the flange crimping process, the increase in flange thickness will significantly reduce the maximum bend-ing stress of the insulator and increase its safety margin. It provides a good design idea for the design of crimping composite insulators.

Background: More than 270 million participants and 128,893 professional players play soccer. Research only weakly supports the impact of diets and supplements on the performance and recovery of professional soccer players. Methods: We conducted a comprehensive search in Pub-Med, Scopus, Web of Science, and clinical trial registers. Inclusion criteria focused on professional or semi-professional soccer players, nutrition or diet interventions, performance improvement outcomes, and randomized clinical trial study types. We assessed quality using the Risk of Bias 2 (RoB 2) tool. We identified 16 eligible articles involving 310 participants. No nutritional intervention during the recovery period effectively improved recovery. However, several perfor-mance-based interventions showed positive effects, such as tart cherry supplementation, raw pistachio nut kernels, bicarbonate and mineral ingestion, creatine supplementation, betaine consumption, symbiotic supplements, and a high carbohydrate diet. These interventions influenced various aspects of soccer performance, including endurance, speed, agility, strength, power, explosiveness, and anaerobic capacity. Conclusions: Specific strategies, such as solutions with bicarbonate and minerals, high carbohydrate diets, and supplements like creatine, betaine, and tart cherry, can enhance the performance of professional soccer players. These targeted nutritional interventions may help optimize performance and provide the competitive edge required in professional soccer. We did not find any dietary interventions that could enhance recovery during recovery.

This paper reports a novel fabric phase sorptive extraction-high performance liquid chromatography-ultra violet detection (FPSE-HPLC-UV) method for the simultaneous extraction and analysis of four phenyltin derivatives that include triphenyltin hydroxide, triphenyltin acetate, triphenyltin chloride and tetraphenyltin in environmental water (agricultural waste water and municipal waste water) and canned food samples. The selected analytes were well resolved by Waters Nova pack C18 column (3.9 x 150 mm, 4 µm particle size) in isocratic elution mode within 15 minutes. The new microextraction media has been analytically evaluated using phenyltin derivatives as model compounds. The factors affecting the extraction efficiency of FPSE have been evaluated and the optimum extraction conditions were determined. Under these optimum conditions, the limits of detection (LODs) for sol-gel C18 coated fabric phase sorptive extraction (FPSE) media in combination with HPLC-UV for the analysis of the phenyltin derivatives were in the range of 10-100 ng/mL with high precision (low relative standard deviation) at 10 ng/mL concentration with good absolute recoveries. To the best of our knowledge, this is the first FPSE extraction procedure applied to environmental water and canned food samples for the simultaneous determination of phenyltin derivatives and could be readily adopted as a rapid and robust green analytical tool for routine environmental and food analytical laboratories.

Emotional intelligence may help nurses to cope with demanding work environments. Although it is well known that emotional intelligence is positively related to work performance literature on nurses is limited. The aim of our study was to examine the impact of emotional intelligence on work performance in a sample of nurses in Greece. We conducted a cross-sectional study in Greece. We collected data from a convenience sample of nurses during January 2024. We measured emotional intelligence with the Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF), and work performance with the Individual Work Performance Questionnaire (IWPQ). We created multivariable linear regression models adjusted for sex, age, educational level, and work experience. We found that the four streams of emotional intelligence (i.e. well-being, self-control, emotionality, sociability) increases nurses’ work performance. In particular, we found a positive relationship between well-being and task performance and contextual performance. Similarly, there was a positive relationship between self-control and task performance. Additionally, sociability increased task performance and contextual performance. Moreover, emotionality and sociability reduced counterproductive work behavior. Our multivariable models identified a positive impact of emotional intelligence on nurses’ work performance. Nurses managers and healthcare organizations should adopt appropriate interventions to improve nurses’ emotional intelligence.

Pump-as-turbines (PAT) have been widely used during the last decade as one of the most interesting technologies in the field of energy recovery. A large number of studies and papers have been published in which the performance of the pumps in the turbine operating regime are analysed. Since horizontal single stage centrifugal norm pumps are most commonly used as PATs, their performances are analysed in this paper. Most of the research was related to individual pump aggregates or smaller groups and to obtaining their performance curves in turbine random mode. In this work, extensive experimental, numerical and theoretical investigations are conducted to obtain complete dimensionless performance characteristics of the single stage centrifugal norm pumps operating as turbines. The main goal of this research was to form a simple analytical expression that will, for this type of aggregate, map the pump operating characteristic to the appropriate turbine operating regime. By using the expressions obtained and presented in the work, engineers are enabled to make the appropriate choice of pump aggregate for operation in the turbine random mode for potential locations. This can allow us to quickly make selection of potential locations and to carry out appropriate techno-economic analyses and analyses of possible energy savings.

In recent years, with the increasing prominence of environmental and social issues, investors have been paying more attention to the ESG performance of enterprises, highlighting the importance of ESG factors in the financial field. This study is based on the theories of banking business models, stakeholder theory, risk management theory, and ESG investment theory. It uses the financial data and ESG scores of Chinese listed banks to deeply analyze the ESG factors and explore their impact on the liquidity risk of commercial banks. The research found that (1) good ESG performance can reduce the liquidity risk commercial banks face by improving bank value and financial performance. (2) ESG factors can also enhance the liquidity management level of commercial banks through standardization and sustainable business principles, thereby reducing the occurrence and impact of liquidity risk. Therefore, it is necessary to reduce the liquidity risk of banks and promote the sustainable development of commercial banks from four aspects: ESG performance and management, bank value, financial performance, and policy regulation.

The purpose of this study is to examine the match-related performance (scores and penalties) of the athletes in the Junior EJC (European Judo Championship) and U23 EJC categories before and immediately after the Covid-19 quarantine, according to gender and weight category groups. Our sample was based on the junior and U23, male and females, 2019-2020 Junior European Judo Championships and 2019-2020 U23 European Judo Championships. A total of 1431 matches in these tournaments were analyzed: male (n=812) and female (n=619). The main findings of this study showed that most matches in both the male and female categories resulted in ippon (2019-2020 average: 64,35% for Junior male, 70,55% for U23 male and 69,3% for Junior female, 64,4% for U23 female). No significant differences (p &gt; 0.05) were observed between gender and age groups concerning the match scores. In the formation phase of the technique, considerations of high- and low-intensity actions, and analysis of the speed of athletes during matches in official competitions can help to better understand the dynamics of the variability of movements in judo matches. In addition, suggestions are presented to be applied to athletes in different age categories who are on the way to become Olympic athletes.

Transcriptome assembly and functional annotation are essential for understanding gene expression and biological function. Nevertheless, many existing tools lack the flexibility to integrate both short- and long-read sequencing data or fail to provide a complete, customizable workflow for transcriptome analysis. Here, we present TrAnnoScope, a comprehensive transcriptome analysis pipeline that provides a complete, customizable workflow capable of efficiently processing and integrating short- and long-read sequencing data to generate high-quality, full-length transcripts with detailed functional annotation. The pipeline encompasses steps from quality control to functional annotation, employing a range of tools and established databases, such as SwissProt, Pfam, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes. As a case study, TrAnnoScope was applied to RNA-Seq data from zebra finch brain, ovary, and testis tissues. TrAnnoScope produced comprehensive transcriptome, demonstrating strong alignment with the reference genome (99.63%) and capturing a significant proportion of nearly complete protein sequences (92.7%). The functional annotation process yielded extensive matches to known protein databases and successfully assigned relevant functional terms to majority of the transcripts. As such, TrAnnoScope successfully integrates multiple sequencing technologies to generate comprehensive transcriptomes with minimal user input. Its modular design, flexibility, and ease of use make it a valuable tool for researchers working with complex datasets, particularly for non-model organisms.

This study presents a detailed comparative analysis of Molecular Docking data, focusing on the binding interactions of conventional ligands and natural compounds, Apigenin and Luteolin, with TP-53, pRb, and APOBEC. Utilizing advanced bioinformatics techniques, coupled with Ar-tificial Intelligence software and High-Performance Computing (HPC), we measured and con-trasted the binding energies of these interactions. Additionally, we investigated protein-protein interactions between HPV-16 oncoprotein E6 and tumor suppressors TP-53 and pRb. Our findings demonstrate that the natural compounds Apigenin and Luteolin exhibit significantly higher binding affinities to TP-53, pRb, and APOBEC compared to conventional pharmacological ligands. The binding energies for Apigenin and Luteolin were approximately -6.9 kcal/mol and -6.6 kcal/mol, respectively, indicating their strong potential as therapeutic agents in inhibiting the oncogenic functions of HPV-16. In contrast, conventional ligands showed lower binding affinities, with energies around -4.5 to -5.5 kcal/mol. The study further revealed that the protein-protein interactions between E6 and TP-53, and E6 and pRb, exhibited considerably higher binding en-ergies, approximately -976.7 kcal/mol, due to the multiple interaction sites and the complex nature of protein interfaces. A conversion formula was developed to translate these high-energy inter-actions to a comparable scale for non-protein-protein interactions, highlighting the superior binding potential of natural compounds, which, through the same conversion formula, was shown to be higher than that of interaction between E6 with TP-53 and E6 with pRb. These results underscore the therapeutic promise of Apigenin and Luteolin in preventing HPV-16-related on-cogenesis. By demonstrating their higher binding affinities to crucial tumor suppressors, this study supports the development of natural compound-based therapies. The findings also em-phasize the necessity for further experimental validation to explore these compounds' efficacy in clinical settings. This comprehensive analysis provides a robust framework for understanding the comparative binding interactions and lays the groundwork for innovative therapeutic strategies against HPV-16.

In this age of global warming, academics and policymakers are increasingly concerned about firm environmental sustainability success. Therefore, this study aims to investigate whether Environmental, Social and Governance (ESG) performance impacts sustainability performance through the mediating effect of firm innovation. To this end, Structural Equation Modeling (SEM) was deployed to analyze data collected from the employees of manufacturing industries in an emerging economy like Bangladesh. The results revealed that ESG performance significantly enhances the innovation and sustainability performance of manufacturing industries, indicating that the higher the ESG performance of a firm, the greater its innovation and sustainability performance. Furthermore, the results confirmed that firm innovation performance fully mediates the relationship between ESG initiatives and sustainability performance. The findings of this study provide policymakers and industry authorities with valuable insight into the role of ESG and innovation performance in improving sustainability performance. Specifically, the study sheds knowledge on how firm ESG initiatives and innovation performance impact sustainability performance in the manufacturing sector of an emerging economy like Bangladesh.

High dimensional statistical analysis for the yield of large-scale circuits is quite difficult due to expensive simulations, especially for the memory circuits with high sigma requirement (e.g., SRAM). In this paper, we developed an efficient sparse additive model to substitute simulations. To fit high sigma region accurately, the modeling center is moved to near failure boundary searched by scaling the shape of sampling function. To solve the model efficiently, the process variables are grouped by standard cells so that the model can be solved by our developed blockwise greedy algorithm. The experiments on the 28nm memory circuits validate that our method achieves high accuracy and efficiency compared with other state-of-art works.

Cool roofs are roofing systems designed to reflect significant solar radiation, reducing heat absorption and subsequent cooling energy demands in buildings. This paper provides a comprehensive review of cool roof technologies, covering performance standards, material options, energy-saving potential, and hygrothermal considerations. The review examines provisions in current codes and standards, which specify minimum requirements for solar reflectance, thermal emittance, and solar reflectance index (SRI) values. These criteria often vary based on factors like roof slope, climate zone, and building type. Different cool roof materials are explored, including reflective paints and coatings that can be applied to existing roofs as cost-effective solutions. Several studies demonstrated the energy performance benefits of cool roofs, showing significant reductions in cooling loads, indoor air temperatures, peak cooling demand, and overall cooling energy consumption compared to traditional roofs. However, hygrothermal performance must be evaluated, especially in cold climates, to optimize insulation levels and avoid moisture accumulation risks, as reduced heat absorption can alter moisture migration patterns within the building envelope. While cool roofs provide substantial energy savings in hot climates, further research is needed to validate modeling approaches against real-world studies, investigate the impact of seasonality and green spaces on cool roof efficacy and urban heat island mitigation, and explore energy savings potential, moisture control, and condensation risks in cold and humid environments.

Ice and snow on roads in winter endanger traffic safety. The existing methods of snow and ice removal have various drawbacks, which need further research. Aiming at solving limitations about high labor intensity, low efficiency, serious environmental pollution of traditional snow and ice removal methods, the anti-icing coating technology of high-voltage conductors is used for reference, and in view of the characteristics of asphalt pavement deicing, an ice-suppressing material that can effectively reduce the adhesion between snow, ice and pavement is successfully developed. As there is no perfect evaluation index and method for ice-suppressing materials at present, this paper further evaluates the performance of ice-suppressing materials from the aspects of deicing performance, durability, adhesion and the impact of ice-suppressing materials on pavement performance. The test results show that the ice-suppressing material can effectively reduce the adhesion between the ice layer and the specimen, making it easier to remove. The ice-suppression material has good slow-release performance, and the specimens still have good deicing performance after 7 cycles of tests.

In the current study, we assessed the effects of conventional and Ultra High Dose Rate (UHDR) irradiation on behavioral and cognitive performance one month following exposure and assessed whether these effects were associated with alterations in the number of immune cells in the hippocampus using flow cytometry. Conventional- and UHDR-irradiated mice showed impaired novel object recognition. During fear learning, conventional- and UHDR-irradiated mice moved less during the inter-stimulus-interval (ISI) and UHDR-irradiated mice also moved less during the baseline period (prior to the first tone). In irradiated mice, reduced activity levels were also seen in the home cage; conventional- and UHDR-irradiated mice moved less during the light period and UHDR-irradiated mice moved less during the dark period. Following behavioral and cognitive testing, infiltrating immune cells in the hippocampus were analyzed by flow cytometry. The percent Ly6G+ CD45+ cells in the hippocampus was lower in conventional- and UHDR-irradiated than sham-irradiated mice, suggesting that neutrophils might be particularly sensitive to radiation. The percent of Ly6G+ CD45+ cells in the hippocampus was positively correlated with the time spent exploring the novel object in the object recognition test, suggesting that the reduced percent Ly6G+ CD45+ cells in the hippocampus might mediate some of the detrimental radiation-induced cognitive effects.

The Inefficient water use, varying and low productivity in Kenya public irrigation schemes is a major concern. It is therefore necessary to periodically monitor and evaluate the performance of public irrigation schemes. The performance of public irrigation in western Kenya was assessed by combining benchmarking methodology and principal component analysis. The aim was to quantify and rank the performance of pumped public irrigation schemes in Kenya. Eleven benchmarking indicators were computed for the period from 2012 to 2016 and compared to global benchmark values. The indicators used fall under agricultural productivity, water supply and financial performance categories. The computed agricultural productivity was 36%–51% in Ahero, 23%–42% in West Kano and 26%–50% Bunyala irrigation scheme. Water supply performance in Ahero, West Kano and Bunyala irrigation schemes varied from 24% to 58%, 3% to 49% and 19% to 43% respectively. Financial performance varied from 46% to 54% in Ahero, 25% to 32% in West Kano and 54%–56% in Bunyala irrigation scheme. An average overall performance efficiency of 46%, 39% and 31% was obtained in Ahero, Bunyala and West Kano irrigation schemes respectively. The performance of the irrigation schemes is very poor and measures on improving performance are needed.

Background: Chronic exposure to severe hypoxia causes an increase in hematocrit (Hct) and hemoglobin concentration ([Hb]), which can lead to Excessive Erythrocytosis (EE), which could impact physical performance. This work aims to determine the differences in the six-minute walking test (6MWT) outputs between EE and healthy subjects residing at more than 5000 meters. Methods: A prospective, cross-sectional study was performed on 71 men (36 healthy and 25 suffering EE) living in La Rinconada, Peru (5100 m). Basal levels of [Hb] and Hct were obtained. All the subjects per-formed the 6MWT, and distance reached, vital signs, dyspnea, and fatigue (Borg scale) at the end of the test were recorded. Results: The average [Hb] and Hct levels in the control group were 18.7 ± 1.2 g/dL and 60.4 ± 7.1%, respectively, contrasting with EE subjects who showed 23.4 ± 1.6 g/dL and 73.6 ± 5.9% (p < 0.001). However, no statistically significant differences were observed in BMI or other anthropometric parameters. At the end of the 6MWT, the distance traveled, and vital constants were similar between both groups, except for arterial oxygen saturation which was consistently lower in subjects with EE along the test. Conclusion: EE does not significantly affect 6MWT performance at high altitudes nor the hemodynamic control during moderate aerobic exercise of subjects who live permanently in a severe hypoxic environment.