Articles

<b>Background:</b> Building Information Modelling (BIM) improves safety planning in construction by enabling visualization and simulation to identify and reduce risks. However, its adoption in Nigeria is limited. This study examines the application of BIM in enhancing safety and environmental performance on construction sites in Nigeria. <b>Methodology:</b> A quantitative cross-sectional survey was conducted using a structured online questionnaire distributed to professionals in Nigeria’s construction industry. A purposive sampling method was employed to target respondents with relevant BIM experience. Data were analysed using SPSS version 28, applying descriptive statistics, chi-square tests, and logistic regression at a 5% significance level. <b>Result:</b> Findings show that BIM was fully adopted by 7.0% of organizations, with only 19.8% of respondents using it to identify safety hazards during planning. While 76.8% reported no notable safety benefit, 19.5% identified improved risk management as the key benefit. Most respondents (80.2%) reported no noticeable environmental benefits. Among those who did, improved energy efficiency was the most cited benefit (16.4%). Respondents with 10 or more years of experience were significantly more likely to report enhanced safety and environmental outcomes (AOR = 4.555; p = 0.003) and adequate BIM utilization (AOR = 3.255; p = 0.023). Those with intermediate BIM experience were also more likely to report high enhancement (AOR = 2.857; p = 0.039) and effective tool use (AOR = 2.881; p = 0.050). <b>Conclusion:</b> This study revealed that BIM has the potential to improve construction outcomes in Nigeria if supported by training, experience, and structured implementation.

<b>Background</b>: Disasters such as typhoons, floods, and earthquakes frequently impact the Philippines, placing nurses at the forefront of response and care. Given these recurring threats, ensuring that nurses possess adequate awareness, knowledge, and skills is crucial to mitigate risks, enhance preparedness, and promote effective disaster management. <b>Aim/Objectives</b>: This study sought to (1) assess the current levels of awareness, knowledge, skills, and involvement of private hospital nurses in Rizal Province across four phases of disaster management—mitigation and prevention, preparedness, response, and rehabilitation and recovery; and (2) propose a contingency and training plan based on identified gaps. <b>Methods</b>: A descriptive correlational design was employed. A total of 350 nurses from Level 1, 2, and 3 hospitals participated by completing a validated questionnaire. Data were analyzed using descriptive statistics, analysis of variance, and correlation tests to identify differences and relationships among variables. <b>Results</b>: Overall, the nurses reported very high levels of awareness and skills, coupled with a high level of knowledge and significant involvement in disaster-related activities. Nurses in larger (Level 3) hospitals exhibited higher practical readiness and engagement, while those in Level 1 and 2 facilities had comparatively lower scores. Positive correlations emerged between higher levels of awareness, knowledge, and skills and increased engagement in disaster initiatives. <b>Conclusion</b>: Building on these findings, a targeted contingency and training plan was designed using Pucel’s Performance-based Instructional Design, emphasizing hands-on simulations, structured policy briefings, and collaborative efforts with local disaster risk reduction offices. Addressing these specific gaps can bolster hospital preparedness, strengthen community resilience, and ensure more effective disaster response and patient care.

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

The SAP HANA Application to handle operational workloads that are consistent with transactions while also supporting intricate business analytics operations. Technically speaking, the SAP HANA database is made up of several data processing engines that work together with a distributed query processing environment to provide the entire range of data processing capabilities. This includes graph and text processing for managing semi-structured and unstructured data within the same system, as well as classical relational data that supports both row- and column-oriented physical representations in a hybrid engine. The next-generation SAP Business Suite program designed specifically for the SAP HANA Platform is called SAP S/4HANA. The key features of SAP S/4HANA are an intuitive, contemporary user interface (SAP Fiori); planning and simulation options in many conventional transactions; simplification of business processes; significantly improved transaction efficiency; faster analytics.

The swift changes in the complexity of the project, scarcity of resources, and pressure on the organization have demonstrated the necessity of more intelligent and data-driven planning and scheduling methods. Conventional methods tend to be less effective in the tasks of uncertainty management, dynamic environment, and dynamism of the project ecosystems of the modern world. The application of machine learning (ML) in the planning and scheduling of projects is thoroughly reviewed in this paper, and it is evident that ML may help to improve the accuracy of forecasts, optimize resource distribution, and make decisions. This paper offers an analysis of the basic concepts of project management, evidence-based practices, and how supervised and unsupervised learning, as well as reinforcement learning concepts, can be combined in the scheduling process. Furthermore, the new literatures are examined to bring out new techniques, including predictive modelling, optimization algorithms, simulation-based planning and adaptive scheduling models. The review determines the main strengths, weaknesses, and gaps in the research that impact the adoption of ML-driven approaches. All in all, the paper describes the ways in which machine learning technologies can change the traditional way of project management by creating efficient, resilient, and proactive planning systems.

The accurate estimation of Individual Wave Components (IWC) is crucial for automated diagnosis of the human digestive system in a clinical setting. However, this process can be challenging due to signal contamination by other signal sources in the body, such as the lungs and heart, as well as environmental noise. To address this issue, various denoising techniques are commonly employed in bowel sound signal processing. While denoising is important, it can increase computational complexity, making it challenging for portable devices. Therefore, signal processing algorithms often require a trade-off between fidelity and computational complexity. This study aims to evaluate an IWC parameter extraction algorithm that was previously developed and reconstruct the IWC without denoising using synthetic and clinical data. To that end, the role of a reliable model in creating synthetic data is paramount. The rigorous testing of the algorithm is limited by the availability of quality and quantity recorded data. To overcome this challenge, a mathematical model has been proposed to generate synthetic bowel sound data that can be used to test new algorithms. The proposed algorithm’s robust performance is evaluated using both synthetic and clinically recorded data. We perform time-frequency analysis of original and reconstructed bowel sound signals in various digestive system states and characterize the performance using Monte Carlo simulation when denoising is not applied. Overall, our study presents a promising algorithm for accurate IWC estimation that can be useful for predicting anomalies in the digestive system.

We study the using the Monte Carlo method and its application. Below are several examples of software implementations of the Monte Carlo method for performing calculations that will allow us to determine the necessary information in cases where probability can be applied. Below is a software implementation of the examples in the C# programming language. The programs have a desktop interface and allow us to calculate such values as the number π and the time required to perform certain actions.

There is a growing body of evidence that the number of individuals suffering from chronic and acute pain is under-reported and the burden of the veteran, aging, athletic, and working populations is rising. Current pain management is limited by our capacity to collaborate with individuals continuing normal daily functions and self-administration of pain treatments outside of traditional healthcare appointments and hospital settings. In this review, the current gap in clinical care for real-time feedback and guidance with pain management decision-making for chronic and post-operative pain treatment is defined. We examine the recent and future applications for predictive analytics of opioid use after surgery and implementing real-time neural networks for personalized pain management goal setting for particular individuals on the path to discharge to normal function. Integration of personalized neural networks with longitudinal data may enable the development of future treatment personalizations paired with electrical simulations.

Estimation of crop water requirements is of paramount importance towards the management of agricultural water resources, which is a major mitigating strategy against the effects of climate change on food security. South Africa water shortage poses a threat on agricultural efficiency. Since irrigation uses about 60% of the fresh water available, it therefore becomes important to optimise the use of irrigation water in order to maximize crop yield at the farm level in order to avoid wastage. In this study, combined application of an artificial neural network (ANN) and a crop – growth simulation model for the estimation of crop irrigation water requirements and the irrigation scheduling of potatoes at Winterton irrigation scheme, South Africa was investigated. The crop-water demand from planting to harvest date, when to irrigate, the optimum stage in the drying cycle when to apply water and the amount of irrigation water to be applied per time, were estimated in this study. Five feed –forward backward propagation artificial neural network predictive models were developed with varied number of neurons and hidden layers and evaluated. The optimal ANN model, which has 5 inputs, 5 neurons, 1 hidden layer and 1 output was used to predict monthly reference evapotranspiration (ETo) in the Winterton area. The optimal ANN model produced a root-mean-square error (RMSE) of 0.67, Pearson correlation coefficient (r) of 0.97 and coefficient of determination (R²) of 0.94. The validation of the model between the measured and predicted ET_o shows a r value of 0.9048. The predicted ETo was one of the input variables into a crop growth simulation model, called CROPWAT. The results indicated that the total crop water requirement was 1259.2 mm/decade and net irrigation water requirement was 1276.9 mm/decade, spread over a 5-day irrigation time during the entire 140 days of cropping season for potatoes. A combination of the artificial neural networks and the crop growth simulation models have proved to be a robust technique for estimating crop irrigation water requirements in the face of limited or no daily meteorological datasets.

<b>Background:</b><b> </b>Currently, the DRL quantity in mammography are evaluated in terms of mean glandular dose (MGD). Since the MGD cannot be measured directly, it can be obtained by calculation using the equation (D=K*g*c*s). In previous studies, the conversion factor g was calculated by Monte Carlo simulation and is not reported from actual measurements. In this study, we focused on the g-factor, which is a conversion factor to the MGD at 50% glandularity, and attempted to measure it using a nanoDot dosimeter to see if it can be used in mammography. <b>Methods: </b>The nanoDot dosimeters were inserted in a PMMA phantom at depths ranging from 0 cm to 6 cm in 1 cm increments, and measurements were made in three HVLs of 0.3 mmAl, 0.35 mmAl, and 0.4 mmAl HVL. The g-factor was calculated from the nanoDot dosimeter values using a conversion equation. <b>Results and Discussion:</b> The measured g-factors for all the HVLs were in close agreement with those of Dance et al. The values of the previous studies did not include the backscatter factor, which may have underestimated the MGD. The difference was smaller for the 0.4 mm Al. Compared to the other HVLs, the 0.4 mm Al was measured without a compression plate, which may have been influenced by the presence or absence of a compression plate. <b>Conclusion:</b> The nanoDot dosimeters were used to calculate g-factors. The results agreed with those of previous studies within uncertainty. This indicates that nanoDot dosimeters can be used in the mammography field.

A spherical shape ultra-wideband antenna is a microstrip patch antenna whose emitted signal bandwidth exceeds the lesser of 500 MHz. One of the major issues hindering the ultra-wideband antennas is poor diversity factors, poor voltage standing wave ratio and poor power efficiency to transmit the required signals. In this research work, the method of approach is the design and analysis of a spherical shape ultra-wideband antenna with the use of computer simulation technology (CST). This antenna is working under the resonant frequency of 6 GHz on a frequency bandwidth of 4-9 GHz. However, this research work has made an intensive review of related works. A spherical shape microstrip antenna with a diameter of 13mm and a radius of 6.5mm was designed, after which a simulation was carried out using the computer simulation technology software. The result from the radiated power shows how high the radiative efficiency is and from the results we were able to observe that the ultra-wideband antenna uses a very low amount of power but can transmit a better outgoing power from the 0.5 watts stimulated power. In this research work, an evaluation process on the envelope correlation coefficient of the antenna s-parameters was carried out, with a good result was obtained. Most importantly the diversity gain of the antenna proves to be good and efficient due to the effectiveness of the antenna radiation efficiency. The results of this antenna produce a very good voltage standing wave ratio (VSWR), the voltage standing wave ratio of this spherical ultra-wideband antenna is less than 2% with a very low return loss reflection. In conclusion, the spherical shape antenna is good for ultra-wideband purposes because of its robustness in delivering high-quality signals with a very low return loss. So, it stands the chance of recommendations in the communication industries due to its high radiation efficiency rate and good VSWR.

The purpose of the study was to examine techniques and strategies use in assessing Social Studies concepts in the three senior high schools in Sefwi Wiawso Municipality in the Western North Region of Ghana. Qualitatively, the research design for this study was a Case study. The population for this study consisted of all ten (10) Social Studies teachers in three public Senior High Schools (Wiawso Senior High School, Wiawso Senior/Technical school, and Asawinso senior high school) in Sefwi Wiawso municipality in the Western North Region of Ghana. Purposive and convenient sampling techniques were used to select municipalities, schools, and Social Studies teachers for the study. The main instrument used for data collection was interview. The data were edited before being analyzed in themes and pattern. Being a case study design in qualitative research, the researcher read through the data several times so as to familiarize with the data, making notes, referring back to the research question, making decisions whether to focus on individual responses or on topics. The interview data were interpreted to articulate meaning, making decisions on whether to use direct quotes or the summary of respondents’ responses and reporting the data considering the interest of various audiences. The study revealed that teachers used various teaching techniques such as brainstorming, simulation, discovery-learning, role-play discussion, dramatization, problem-solving, and building a community implies that they believed in child-centered method of the teaching and learning of Social Studies. The study also concluded that Social Studies teachers use techniques and strategies like concept attainment, field trips, and debate co-operative learning in motivating and providing learners with the opportunity to interact with their own community or environment. It is recommended that Social Studies teachers should make effort either to go on field trips or make use of available experts that are in their communities by inviting them into the Social Studies classrooms to tap into their rich knowledge to enhance concept learning. It is also recommended that frequent seminars and periodic or regular in-service training should be organised by the Ghana Education Service to help Social Studies teachers to acquaint themselves with the modern teaching techniques and strategies that promote the teaching and learning of Social Studies concepts.

In military battle, the unmanned combat aerial vehicle (UCAV) plays a critical role. The UCAV avoids the fatal military zone as well as radars. If there is just a narrow path between the defensive areas, it is dan-gerous. It chooses the quickest and safest path. The balance evolution technique is used to improve the path planning of UCAV in this study, which results in a novel artificial bee colony. To regulate the position of a swarm of UCAVs, a particle swarm network is used to communicate between the UCAVs in the swarm. According to simulation data, the particle swarm network technique is more efficient than the ABC ap-proach. The intelligence system is taught via an artificial neural network.

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

The present paper attempts to investigate the problem of Fault Tolerant Control for a class of uncertain neutral time delay systems. In the first time, we consider an additive control that is based on adding a term to the nominal law when the fault occurs. This approach will be designed in three steps. The first step is fault detection while the second one is fault estimation. For these two steps, we consider the adaptive observer to guarantee the detection and estimation of the fault. The third step is the fault compensation. Lyapunov method and Linear Matrix Inequality (LMI) techniques were considered to improve the main method. Second, we propose a Pseudo Inverse Method "PIM" and determine the error between the closed loop and the nominal system. Finally, simulation results are presented to prove the theoretical development for an example of an uncertain neutral time delay system.

SARS-COV-19 is a serious respiratory infection created by a devastating coronavirus family (2019-nCoV) that has become the first global epidemic of the last one hundred years. It is a highly transmissible virus transmitted by inhalation or contact with the droplet core produced by infected people when they sneeze, cough, and speak. SARS-COV-2 transmission in the air is possible even in a confined space near the infected person. This study aimed to evaluate the effectiveness of using a shield or mask as a barrier to a patient’s face against the spread of virus particles. For the present simulation, the discrete phase model (DPM) is used; Because this model allows us to study the particle’s mass discretely in a fluid space with the continuous phase. Due to the choice of this model, the virus particles secreted from the patient’s mouth are considered a discrete phase, and the open airflow in the computational area is considered a continuous phase. The present study uses fluent 2019R3 software to simulate the virus transmission to model the transient flows numerically. The analysis found that the masks or shields can be an effective method of protecting the participants of a conversation in the presence of an infected person.

This research explores the complex relationship between user-perceived Quality of Experience (QoE) and underlying network performance for multimedia traffic. As video streaming, online gaming, and interactive media dominate modern networks, ensuring consistent QoE has become a key challenge. The study develops a network performance model that integrates objective Quality of Service (QoS) parameters—such as delay, jitter, packet loss, and throughput—with subjective QoE metrics like Mean Opinion Score (MOS) and perceptual quality indices. Using simulation-based and analytical approaches, the paper evaluates how network conditions affect multimedia traffic behavior and user satisfaction. The results highlight critical thresholds for QoE degradation, enabling predictive modeling for adaptive multimedia delivery and real-time optimization. This work contributes to designing intelligent, user-centered network management systems capable of balancing resource efficiency and end-user satisfaction.

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