This study explores the primary drivers influencing sustainable project management (SPM) practices in the construction industry. This research study seeks to determine whether firms are primarily motivated by external pressures or internal values when embracing SPM practices. In doing so, this study contributes to the ongoing discourse on SPM drivers by considering coercive pressures (CP), ethical responsibility (ER), and green transformational leadership (GTL) as critical enablers facilitating a firm’s adoption of SPM practices. Based on data from 196 project management practitioners in Pakistan, structural equation modeling (PLS-SEM) was employed to test the hypothesized relationships. Results highlight that CP influences the management of sustainability practices in construction projects, signifying firms’ concern for securing legitimacy from various institutional actors. As an ‘intrinsic value’, ER emerges as a significant motivator for ecological stewardship, driven by a genuine commitment to promoting sustainable development. This study also unveils the significant moderating effect of GTL on the association among CP, ER, and SPM. Lastly, the results of IMPA reveal that ER slightly performs better than CP as it helps firms internalize the essence of sustainability. This research study expands our understanding of SPM drivers in construction projects by exploring the differential impact of external pressures and the firm’s intrinsic values. These findings provide valuable insights for policymakers and practitioners, aiding them in promoting SPM to attain sustainable development goals.

To save patients’ lives, it is important to go for an early diagnosis of intracranial hemorrhage (ICH). For diagnosing ICH, the widely used method is non-contrast computed tomography (NCCT). It has fast acquisition and availability in medical emergency facilities. To predict hematoma progression and mortality, it is important to estimate the volume of intracranial hemorrhage. Radiologists can manually delineate the ICH region to estimate the hematoma volume. This process takes time and undergoes inter-rater variability. In this research paper, we develop and discuss a fine segmentation model and a coarse model for intracranial hemorrhage segmentations. Basically, two different models are discussed for intracranial hemorrhage segmentation. We trained a 2DDensNet in the first model for coarse segmentation and cascaded the coarse segmentation mask output in the fine segmentation model along with input training samples. A nnUNet model is trained in the second fine stage and will use the segmentation labels of the coarse model with true labels for intracranial hemorrhage segmentation. An optimal performance for intracranial hemorrhage segmentation solution is obtained.

Herein, we report a facile preparation of super-hydrophilic sand by coating the sand particles with cross-linked polyacrylamide (PAM) hydrogels for enhanced water absorption and controlled water release aimed at desert agriculture. To prepare the sample, 4 wt% of aqueous PAM solution is mixed with organic cross-linkers of hydroquinone (HQ) and hexamethylenetetramine (HMT) in a 1:1 weight ratio and aqueous potassium chloride (KCl) solution. A specific amount of the above solution is added to the sand, well mixed, and subsequently cured at 150 °C for 8 h. The prepared super-hydrophilic sands were characterized by Fourier-transform infrared spectroscopy (FT-IR) for chemical composition and X-ray diffraction (XRD) for successful polymer coating onto the sand. The water storage for the samples was studied by absorption kinetics at various temperature conditions, and extended water release was studied by water desorption kinetics. The water swelling ratio for the super-hydrophilic sand has reached a maximum of 900% (9 times its weight) at 80 °C within 1 h. The desorption kinetics of the samples showed that the water can be stored for up to a maximum of 3 days. Therefore, super-hydrophilic sand particles were successfully prepared by coating them with PAM hydrogels, which have great potential to be used in sustainable desert agriculture.

The effects of climate change are already being felt, including the failure to harvest several agricultural products. On the other hand, peatland requires good management because it is a high carbon store and is vulnerable as a contributor to high emissions if it catches fire. This study aims to determine the potential for livelihood options through land management with an agroforestry pattern in peatlands. The methods used are field observation and in-depth interviews. The research location is in Kuburaya Regency, West Kalimantan, Indonesia. Several land use scenarios are presented using additional secondary data. The results show that agroforestry provides more livelihood options than monoculture farming or wood. The economic contribution is very important so that people reduce slash-and-burn activities that can increase carbon emissions and threaten the sustainability of peatland.

Modelling and simulation have now become standard methods that serve to cut the economic costs of R&D for novel advanced systems. This paper introduces the study of modelling and simulation of the infrared thermography process to detect defects in the hydroelectric penstock. A 3-D penstock model was built in ANSYS version 19.2.0. Flat bottom holes of different sizes and depths were created on the inner surface of the model as an optimal scenario to represent the subsurface defect in the penstock. The FEM was applied to mimic the heat transfer in the proposed model. The model’s outer surface was excited at multiple excitation frequencies by a sinusoidal heat flux, and the thermal response of the model was presented in the form of thermal images to show the temperature contrast due to the presence of defects. The harmonic approximation method was applied to calculate the phase angle, and its relationship with respect to defect depth and defect size was also studied. The results confirmed that the FEM model has led to a better understanding of lock-in infrared thermography and can be used to detect subsurface defects in the hydroelectric penstock.

Since my country entered the era of Internet economy, the scale of the software industry is gradually expanding, and programming language, as an important tool and idea for software development, is a necessary skill for every development practitioner. Among them, C language, as the basic language of computer software programming, is also an important basic course for cultivating students’ computer application ability in science and engineering majors in colleges and universities. This paper mainly studies the teaching reform of C language programming courses and proposes corresponding optimization measures, so as to lay a solid foundation for the continuous improvement of students’ learning quality of C language courses and the continuous strengthening of programming ability.