Innovative lightweight smart structures incorporating piezoelectric material-based active elements, both as sensors and actuators, have been identified to present manifold advantages over traditional passive systems. Such structures have become intrinsically integrated into smart mechatronic systems, necessitating advanced design, testing, and control techniques. Real-time simulation of shell-type deformable objects, especially when employing the finite element method for non-linear analysis and control, has been challenging due to the extensive computational demand. Presented herein is an efficacious implementation leveraging machine learning with the isogeometric finite element formulation. This implementation focuses on shell-like smart mechatronic structures crafted from composite laminates comprising piezoelectric layers, which are characterised by electro-mechanical coupling. The foundation for the shell kinematics is derived from the Mindlin-Reissner assumptions, effectively incorporating transverse shear effects. While the inclusion of machine learning facilitates real-time efficient operations, the isogeometric finite element analysis (FEA) introduces pronounced advantages over conventional finite element method (FEM), also serving as a valuable source of offline data crucial for the training phases of machine learning algorithms. A piezo-laminated semicircular arch has been analysed to exemplify the effectiveness and performance of the presented methodology. Explorations into further machine learning techniques and intelligent control schemes are also contemplated.

Effective risk management remains pivotal to the success of any project, especially in the oil and gas industry. This study seeks to identify and quantify the potential risks in Oil and Gas Construction Projects (OGCP) within Pakistan. An exhaustive literature review is undertaken to elucidate various risk classifications and factors. Nine risk classifications emerge from this scrutiny: Health, Safety and Environment (HSE), political, legal, regulatory and bureaucratic, labor and human resources, logistics, economic and financial, technological and technical, and security and management. The novelty of this research lies in the adoption of a quantitative approach, a questionnaire rooted in Failure Modes and Effects Analysis (FMEA), asking respondents to quantify risk factors based on severity, occurrence, and detection. The results obtained from the modified FMEA questionnaire indicate that the highest average risks are associated with logistics, health, environment and safety, and legal, regulatory and bureaucratic factors. Meanwhile, political, human resource, management, and technical and technological factors register as the second-highest risks. Security risk records the least average Risk Priority Number (RPN). The most significant risk factors identified include the lack of a disaster management system, depletion of hydrocarbon resources, corruption, contractual breaches, delays in customs clearance, logistic provider complications, design flaws, technical limitations, and contractor incompetence. This research endeavors to provide academia and industry with expansive knowledge related to the risks inherent in these complex projects.

In modern mobile machines, mechatronic systems have been integrated, enabling: a) the automation and robotization of machine tasks, b) the regulation of drive system parameters, and c) the transfer and processing of signals pertaining to machine management and monitoring. This study presents an in-depth analysis of mechatronic systems responsible for drive system regulation, transmission automation, and robotization of mobile machine manipulators. Criteria and objectives for regulation and automation are delineated, based on which application software has been developed. Through these mechatronic systems, efficient, ergonomic, and ecologically sound operations of mobile machines are facilitated.

In this study, the precision of anatomical models and surgical guides pertaining to the knee joint, fabricated using the mSLA technique, was critically examined. The ITK-SNAP program was employed for the segmentation and reconstruction of knee joint anatomical structures, while surgical guide modelling was executed using the Siemens NX program. Subsequent fabrication of the models was accomplished with the Anycubic Photon Mono 4K MSLA 3D printer. An MCA II articulated arm equipped with a laser head, in conjunction with a TalyScan 150 profilometer, was utilized to gauge both the geometrical fidelity and surface roughness of the resulting models. Results indicated that the geometrical precision of these models remained within a tolerance of +/-0.3 mm. With regard to surface roughness, the Sa parameter was observed to lie between 2 and 2.5 µm.

Wood, notably in the forms of sawn lumber and glued laminated (glulam) timber, serves as a prevalent structural material for lightweight constructions and bridges with short spans. Over time, timber structures might experience deterioration due to factors such as biological attack, ageing, and escalated service loads. In such cases, reinforcing or repairing the compromised timber components can often be more economical than full replacement. Fiber-reinforced polymer (FRP) composites, particularly those strengthened using carbon fiber, present significant potential in enhancing the stiffness or load-carrying capacity of these timber systems. In the present investigation, the bending behavior of both solid and glulam beams, reinforced with carbon FRP composites in a "U" shape at the bottom layer, was studied experimentally and numerically. It was observed that reinforced glulam beams exhibit superior load-carrying capacity, displacement, modulus of rupture, and modulus of elasticity as compared to their unreinforced solid beam counterparts. Even though both types of beams are fabricated from identical materials, the laminated beams demonstrated markedly enhanced bending characteristics. Moreover, the addition of reinforcement to glulam beams showed a substantial improvement in bending performance. Consistency between numerical simulations, conducted using a finite element analysis program, and experimental outcomes was noted. This research suggests that timber materials, when strengthened with fiber-augmented polymer fabrics, can be accurately represented using numerical tools.

Project delays pose a substantial challenge in the construction sector. The primary objective of this research is to discern the root causes of project delays in the construction industry and proffer potential solutions, inclusive of the application of building information modeling (BIM) and integrated project delivery (IPD). The integration of IPD and BIM, predicated upon established blueprints, was explored to streamline cost management processes and investigate the potential incorporation of design information within the framework of building price lists. A comprehensive review of extant literature identified 20 possible causes of delays in Iranian construction projects. This study employed a descriptive research design, analyzing data collected from 90 questionnaires completed by construction experts using the statistical package for the social sciences (SPSS) statistical software. A case study of the Dehloran Azad University building project was undertaken, utilizing Revit software for simulation exercises. Field investigations, coupled with a questionnaire disseminated among construction consultants and contractors, elucidated four primary factors contributing to project delays in Iran: 1) the employer's failure to fulfill financial obligations; 2) disregard for the socio-political-economic conditions; 3) absence of a feasibility study prior to tender participation; and 4) inadequate interdepartmental communication. Successful project execution hinges on active team participation and the value that such teamwork brings. The implementation of the IPD model was found to encourage increased enthusiasm and participation. Given that the most significant source of delays in Iran's construction projects was identified as financial issues, the adoption of BIM/IPD may mitigate delays and risks associated with inaccurate estimates. This approach was also found to be effective in projects that are in mid-stage completion.

This investigation elucidates the influence of Administrative Empowerment (AEM) on employee performance within the distribution sector of petroleum products in Torbat Heydarieh, Iran, utilizing a case-study approach to examine the correlational effects of varied AEM factors. A descriptive-analytical methodology was employed, with data collected through a standardized empowerment questionnaire, administered to the entire workforce as the population of interest. The validity of the questionnaire was ensured through the application of the Kolmogorov-Smirnov (K-S) test and Cronbach's alpha, while regression correlation coefficients were used to confirm the legitimacy of the resultant data. A simple random sampling method was employed, yielding a sample size of 45 participants. The principal outcome of this research suggests a consensus regarding the positive influence of AEM on expertise-based outcomes within the Iranian petroleum product distribution sector. Further, the study identified the workplace environment, morale, organizational belongingness, access to knowledge information, and job skills as the most potent determinants influencing human resource motivation. These elements surfaced as critical, feasible, and interesting aspects of work, and were found to be of paramount importance in the empowerment process.

This study focuses on the detailed reconstruction of chain wheel geometry utilizing measurement data gathered from the MarSurfXC20 contact system and the iNEXIVE VMA 2520 optical system. Supplementary data were also gathered from a digital micrometer and a caliper to provide a comprehensive data set for the analyzed geometry. The geometric model of the chain wheel was then constructed using Siemens NX software. The reconstructed model was subsequently compared with the original design specifications to assess the fidelity of the reconstructed model. Results demonstrate a high degree of correlation between the model generated by reverse engineering and the original design model. Despite the satisfactory correlation, potential inaccuracies were identified, necessitating further research to mitigate these discrepancies and optimize the procedures for parameters beyond the established tolerance. The study affirms the feasibility of utilizing contact and optical measuring systems in the reverse engineering process of chain wheel geometry, although it underscores the need for additional refinement to improve the model's accuracy.

With the expansion of steel production via electric arc furnaces, an increase in dust generation—a by-product of these operations—poses substantial challenges. These difficulties stem from land use restrictions for large-scale dust waste storage and the environmental implications of heavy metal contamination inherent in the dust. In an effort to promote the repurposing of this potentially hazardous solid waste, this study examines the concentration and leachability of various heavy metals in this dust. Digestion of the dust samples was carried out in a controlled laboratory setting, after which the concentrations of iron (Fe), magnesium (Mg), zinc (Zn), manganese (Mn), nickel (Ni), lead (Pb), cadmium (Cd), and cobalt (Co) were determined using flame atomic absorption spectrometry. The mean concentrations of these heavy metals in the dust were found to be in the following descending order (in mg/kg): Fe> Mg> Zn> Mn> Ni> Pb> Cu> Cd> Co. Water leaching tests were subsequently conducted, revealing that Co and Cd exhibited the greatest leachability at varying pH levels. Conversely, Fe and Ni displayed minimal leachability. These findings have significant implications for the reuse and environmental management of electric arc furnace dust.

The COVID-19 pandemic emerged over three years ago as a public health crisis, swiftly evolving into a worldwide economic crisis with far-reaching implications for global business enterprises and industries. The unprecedented disruption has varied across sectors, with some experiencing severe consequences while others have thrived. As governments and economies continue to recover from the pandemic's effects, it is crucial to analyze and comprehend these impacts to foster sustainable growth and prepare for future disruptions. This study aims to examine the COVID-19 pandemic's ramifications on Pakistan's software business enterprises by addressing three exploratory research questions: a) the pandemic's influence on Pakistan's software business enterprises, b) actions and initiatives undertaken by these enterprises during the pandemic, and c) the contributions made by these enterprises in combating the pandemic. Employing a mixed-methods approach, a survey research design was developed, incorporating both quantitative and qualitative methods to create a questionnaire grounded in a literature review. The findings of the survey are presented and discussed in-depth. This research contributes to the expanding body of knowledge on the COVID-19 pandemic's effects on business enterprises and industries.

In this study, an intelligent optimization system for laser micro-machining operations is developed, utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS). The heuristic optimization tool, ANFIS, synergistically combines back-propagation training with gradient descent in a unidirectional manner. A comprehensive training set, incorporating experimental data from the literature, highlights the sensitivity of groove depth and recast layer height to specific critical operating factors during the laser micro-machining process. By optimizing lamp current, pulse width, and frequency, the proposed system aims to achieve superior groove depth and recast layer height outcomes. This novel microscopic research holds the potential to captivate both academic scholars and industry professionals.

In the global economy, plastics are considered a versatile and ubiquitous material. It can reach to marine ecosystems through diverse channels, such as road runoff, wastewater pathways, and improper waste management. Therefore, rapid mitigation and reduction are required for this ever-growing problem. The marine habitats are believed to be the highest emitters and absorbers of O₂ and CO₂ respectively. As such, every day, the prominence of managing the litter in the ocean is growing effectively and efficiently. One of the most significant challenges in oceanography is creating a comprehensive meshless algorithm to handle the mathematical representation of waste plastic management in the ocean. This research dedicates to studying the dynamics of waste plastic management model governed by a mathematical representation depending on three components viz. Waste plastic (W), Marine litter (M) and Recycling of debris (R), i.e., WMR model. In this regard, an unsupervised machine learning approach, namely Mexican Hat Wavelet Neural Network (M_hWNN) refined by the efficient Limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm (L-BFGS), i.e., M_hWNN-LBFGS model has been implemented for handling the non-linear phenomena of WMR models. Besides, the obtained solution is meshfree and compared with the state-of-art numerical result to establish the precision of the M_hWNN-LBFGS model. Furthermore, different global statistical measures (MAPE, TIC, RMSE, and ENSE) have been computed at twenty testing points to validate the stability of the proposed algorithm.

Aircraft maintenance is an expansive multidisciplinary field which entails robust design and optimization of extensive maintenance operations and procedures; encompassing the fault identification, detection and rectification, and overhauling, repair or modification of aircraft systems, subsystems, and components, as well as the scheduling for various maintenance operations, in compliance with the aviation standards; in order to predict, pre-empt and prevent failures and thus ensure the continual reliability of aircraft. Advances in Big Data Analytics (BDA) and artificial intelligence techniques have revolutionized predictive maintenance operations. Predictive maintenance is making big strides in the aerospace sector accompanied by a variety of prognostic health management options. Artificial intelligence algorithms have recently been extensively applied to optimize aircraft maintenance systems and operations. Several researchers have proposed, analysed, and investigated the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) based data analytics for predictive maintenance of aircraft systems, subsystems, and components. This paper provides a comprehensive review of the ML techniques like Multilayer Perceptron (MLP), Logic Regression (LR), Random Forest (RF), Artificial Neural Network (ANN), Support Vector Regression (SVR), Linear Regression (LR), and other common ML techniques for their present implementation and potential future applications in aircraft maintenance.