Miracle-dz/UniFAR-FTUs huggingface dataset:
BOARD GOVERNANCE, EARNINGS MANAGEMENT, AND FIRM FINANCIAL SUSTAINABILITY: A CONCEPTUAL OVERVIEW FROM THE PERSPECTIVES OF EMERGING ECONOMIES

The attainment of financial sustainability is of paramount importance for firms since it guarantees their enduring viability and achievement. It enables organisations to navigate periods of economic decline, allocate resources towards expansion prospects, and fulfill their commitments to employees and stakeholders. By focusing on Sustainable Development Goals (SDGs), this study aims to draw a conceptual framework for how board governance affects firm financial sustainability with the mediating effects of earnings management. This study expects board governance to be the significant determinant of firm financial sustainability, and earnings management will act as a key player in the relationship. To the best of our knowledge, this is a unique study that considers the mediating effects of earnings management in the association between board governance and firm financial sustainability. The result of this study will be useful for the regulators in enhancing the board governance issues to deter earnings management and to improve firm financial sustainability. The study will also add value as a battery for future research and studies, particularly in the context of board governance and financial sustainability relationships in emerging as well as developing economies with similar economic perspectives.
Migration velocity analysis using pre‐stack wave fields

Using both image and data domains to perform velocity inversion can help us resolve the long and short wavelength components of the velocity model, usually in that order. This translates to integrating migration velocity analysis into full waveform inversion. The migration velocity analysis part of the inversion often requires computing extended images, which is expensive when using conventional methods. As a result, we use pre‐stack wavefield (the double‐square‐root formulation) extrapolation, which includes the extended information (subsurface offsets) naturally, to make the process far more efficient and stable. The combination of the forward and adjoint pre‐stack wavefields provides us with update options that can be easily conditioned to improve convergence. We specifically use a modified differential semblance operator to split the extended image into a residual part for classic differential semblance operator updates and the image (Born) modelling part, which provides reflections for higher resolution information. In our implementation, we invert for the velocity and the image simultaneously through a dual objective function. Applications to synthetic examples demonstrate the features of the approach.
Cave front tracking experiment

Seismic tomography, a procedure to trace seismic wave velocity variation, has been used for more than 100 years to “see through rock”. Initially the recording of seismic waves from natural sources (earthquakes) using seismographs, provided the first evidence of the crust mantle boundary. Further development of recording techniques and use of explosive seismic sources were employed for the delineation of the geological structures of mineral deposits (primarily oil and gas) as well as detailing the earth mantle location and geometry. Ultra sonic techniques have been applied in the laboratory experiments to characterise rock sample velocities and its relationship to changes in temperature and stress. Active seismic tomography for hydrocarbon resources determination as well as for the earth structure utilized both reflection and refraction techniques. During the early 60’s seismic techniques were employed in active mines to investigate rock properties for geotechnical purposes. The objective of the Cave Front Tracking Experiment was to investigate whether active seismic tomography could potentially be used to track the cave back progression of a block cave mine. A small scale experiment was conducted to study the variations in seismic travel times associated with ray path refraction caused by stress changes and changes in the rock fabric induced by sub-level mining. A piezoelectric transducer was employed as an active seismic source and a seismic recording system was installed to monitor a volume of rock through which sub-level mining was propergated. Implementation involved the deployment of 11 seismic detectors and the recording of pre-stacked data. The experimental details and hardware characteristics are discussed, which includes technical problems encountered, the process and techniques of significant data stacking in order to recover weak signals generated by a piezoelectric seismic source and travel time variation calculation using signal cross-correlation. Signal travel time variation measurements were made over the course of a year and a half (2013-14), however only five months are investigated in detail and related to mining activities. Finite difference numerical modelling was also employed in order to create a better understanding of the sub-level mining process and its effect on seismic signal delay change. Technical problems associated with the signal processing and signal behavior interacting with an advancing mining front were identified and a number of techniques were employed to overcome problems faced by routine cave front estimation in a working mine environment. The field testing of a prototype impact source that is pneumatically driven and an eccentric rotating mass device is discussed. Stellenbosch University https://scholar.sun.ac.za
Aerial identification of flashed over faulty insulator using binary image classification

Flashed over insulator faults are the most significant faults in high voltage line insulators. They are complicated to identify using traditional methods due to their labor-intensive nature. This study proposes a deep learning-based algorithm for detecting flashed over insulator faults in the real time. The algorithm is based on the Resnet 50 architecture, which has been shown to be effective for image classification tasks in the previous studies regarding image analysis. The algorithm is fast, robust and efficient, making it suitable for real-time applications. The algorithm is trained on a dataset of images of flashed over and non-flashed over insulators. This dataset was collected from various transmission lines and National Center of Robotics and Automation, which are located in Pakistan. For validating the effectiveness of the Resnet 50 algorithm, it was compared with the results obtained from the two other widely popular deep learning algorithms, Densenet 121 and VGG 16 (trained and validated on the same dataset). The results showed that the Resnet 50 was able to detect flashed over insulator faults with an accuracy of over 99%. Whereas the Densenet 121 and VGG 16 have achieved an accuracy of less than 51%.
Knowledge of home gardening and anthropometric status of adult women in Odeda Local Government, Abeokuta, Ogun State, Nigeria

A healthy lifestyle and illness prevention depend on adequate nutrition, and homestead food production, including home gardening, is one way to increase access and lower the cost of healthy foods. This study was carried out in Odeda Local Government Area of Ogun State to assess the anthropometric indices and knowledge of home gardening of women. The sample size comprised of 170 respondents which were chosen using a multistage sampling technique. Data on sociodemographic, socioeconomic variables, and home gardening knowledge were obtained using an interviewer-administered, well-structured questionnaire. Anthropometric measurements were taken using standard procedures. The nutritional status was assessed using anthropometric measurements. Version 20.0 of Statistical Product and Service Solutions (SPSS) was used to analyze the data. The findings were presented as frequency, percentage, mean, and standard deviation. The threshold for statistical significance was set at p 0.05. The responders were 33.5 years old on average. The majority of respondents (85.3%) had at least a primary education. It was found that 94.7% of the adult women had good knowledge of home gardening, 4.7% had average knowledge, and 0.6% had low knowledge. The nutritional status of the respondents showed that 24.1 percent were overweight, 10.6 percent were underweight, and 10.6 percent were obese. The home garden knowledge score and the anthropometric measurement (BMI) were shown to be significantly associated. The result of this study is an indication that women who are knowledgeable about gardening at home are more likely to have higher nutritional status.
Learning Gap Assessment in Integrated Mathematics 9

The pandemic has profoundly impacted education, posing unprecedented challenges that demand immediate attention. Thus, this study was conducted to identify intervention activities that may be introduced on the learning gaps in Integrated Mathematics 9 for the First Quarter of the School Year 2022-2023. A quantitative quasi-experimental research using a pretest-posttest design was employed in this study and conducted on the 31 Grade 9 students of St. Paul University Surigao during the First Quarter of the School Year 2022-2023. A validated test was used to conduct the pretest and posttest to assess the learning gaps in Mathematics 9. Frequency, percentage distribution, and paired t-test were used in analyzing the data gathered. This study found that there are least-mastered competencies in the First Quarter of Mathematics 9. In addition, there is a significant difference in the pre-and posttest performance of the learners, especially after giving intervention activities such as drill, practice exercises, tutoring sessions, or small group instruction, peer tutoring and collaborative learning, expanded opportunity, explicit and technology-assisted instruction. Thus, the intervention improved learner performance and addressed least-mastered competencies. It is recommended for mathematics teachers to design further intervention materials targeting other least-learned competencies.
Promoting Resilience in Adolescents from Divorced Family: The Role of Social Support and Self-Efficacy

Divorce can be a tumultuous experience for adolescents; social support and self-efficacy are recognized as crucial factors in helping them navigate the challenges. Resilience, which pertains to an individual's ability to adapt positively to adversity, is essential during this transitional period. This research delves into the evolving understanding of how social support impacts resilience in adolescents from divorced families, with self-efficacy as a mediator. The research involved 400 respondents (318 females and 82 males) aged 15-21 from divorced families. Data were collected using the Resilience Scale (12 items, ), General Self Efficacy Scale (10 items, , and Social Provisions Scale (26 items, . The data analysis techniques applied in this study were descriptive, correlational, and regression-based mediating analyses. The mediating analysis used the PROCESS syntax in IBM SPSS. This study showed that social support increased resilience in adolescents from divorced families, and self-efficacy mediates social support and resilience. This study confirmed that self-efficacy partially mediates social support and resilience in adolescents from divorced families.
Neotectonic intra-plate fault zone mapping and hydrogeology in floodplain sediments: an inter-disciplinary approach

Over the past decade, a relatively rich record of neotectonics has been revealed in continental Australia, however very few investigations into the hydrogeological implications have been undertaken. While the most active intra-plate deformation zones are readily identified by seismicity monitoring and satellite and airborne terrain mapping, advances in airborne electromagnetic (AEM) technology and data optimization have made it possible to map numerous, more subtle ‘blind’ intra-plate fault systems concealed in near-surface floodplain landscapes. To date, fault geometries, displacements, and fault zone properties remain ambiguous due to the combination of AEM footprint resolution, the non-uniqueness of the conductivity models and derived hydrostratigraphy and fault geometry solutions produced by AEM equivalent inversion models, and the inherent uncertainty of stitched 1D AEM inversion models. The resultant uncertainty in fault zone characterisation inhibits investigations into the permeability heterogeneity and anisotropy introduced by these faults, making it difficult to resolve the significance of these structures for groundwater processes. In this study, a novel, inter-disciplinary approach has been developed that helps characterise the hydrogeology of one such intra-plate fault zone in unconsolidated, near-surface floodplain sediments. The approach integrates the mapping of tectonic geomorphology, with mapping of sub-surface hydrostratigraphy and ‘blind’ intra-plate fault systems using AEM. Validation of fault zone geometry and displacement at local scales is provided by ground geophysics (e.g. seismic reflection, resistivity and surface nuclear magnetic resonance (SNMR)) and drilling. Fault zone hydrogeology, including permeability variability, has been assessed through the integration of geophysics with hydrochemistry, hydrodynamics, and studies of vegetation response to water availability (using Landsat time-series analysis). A combination of deterministic and stochastic approaches is then used to unravel complex fault zone conduit-barrier system behaviour that determines lateral and vertical groundwater flow, inter-aquifer leakage and recharge. This inter-disciplinary methodology has been used to parameterise numerical groundwater flow models and target potential groundwater resources.
Predicting depression and suicidal tendencies by analyzing online activities using machine learning in android devices

Artificial Intelligence (AI) has brought about a profound transformation in the realm of technology, with Machine Learning (ML) within AI playing a crucial role in today's healthcare systems. Advanced systems with intellectual abilities resembling those of humans are being created and utilized to carry out intricate tasks. Applications like Object recognition, classification, Optical Character Recognition (OCR), Natural Language processing (NLP), among others, have started producing magnificent results with algorithms trained on humongous data readily available these days. Keeping in view the socio-economic implications of the pandemic threat posed to the world by COVID-19, this research aims at improving the quality of life of people suffering from mild depression by timely diagnosing the symptoms using AI in android devices, especially phones. In cases of severe depression, which is highly likely to lead to suicide, valuable lives can also be saved if adequate help can be dispatched to such patients within time. This can be achieved using automatic analysis of users’ data including text messages, emails, voice calls and internet search history, among other mobile phone activities, using Text mining/ text analytics which is the process of deriving meaningful information from natural language text. Machine Learning models analyse the users’ behaviour continuously from text and voice communications and data, thereby identifying if there are any negative tendencies in the behaviour over a certain period of time, and by using this information make inferences about the mental health state of the patient and instantly request appropriate healthcare before it is too late. In this research, an android application capable of performing the aforementioned tasks in real-time has been developed and tested for various performance features with an average accuracy of 95%.
Lighting Analysis of Single Pendentive Dome Mosque Design in Sarajevo and Istanbul during Summer Solstice

This study analyzes lighting performance in single-pendentive-domed mosques, a type of architectural design common during the Ottoman era through a discussion ofthe Orhan Gazi Mosque in Istanbul, Turkey, and the Ferhadija Mosque in Sarajevo, Bosnia-Herzegovina. Our lighting assessment was conducted during the summer solstice. This study applies simulation analysis to the selected mosques using Autodesk 3ds Max Design 2012, which creates a lighting simulation showing indoor illuminance levels at hourly intervals. A weather file is added during the simulation process to accurately replicate local weather conditions. Our analysis shows that both mosques have illuminance levels of Scale 3 and 4, brightness levels not demanding high visibility and suitable for reading areas respectively; illuminance levels are slightly higher at Ferhadija Mosque than at the Orhan Gazi Mosque. The study concludes that mosques with single-pendentive-domed roofs have interior illuminance levels designed for tasks not demanding hi.
Determining Topographically Controlled Flows through a Combined Contraction and Hollow in the Pearl River Estuary, China

ABSTRACT Liu, H. and Wu, J., 2017. Determining topographically controlled flows through a combined contraction and hollow in the Pearl River estuary, China. The modern Pearl River delta, with a two-fifths area of rocky islands or hills, is estuarine accumulation dominated, where most river-borne sediments are still trapped within the estuary. However, the role of topographic flows in controlling riverine sediment dispersal and delta development is not straightforward. In this paper, the topographically controlled flows through a combined contraction and hollow were investigated to understand their roles in modern delta development. Moving shipboard surveys, bottom-mounted tripods, and water-level gauging along the Yamen Channel were undertaken in the 2012 wet season to measure mean flows, bottom boundary layer flows, tidal elevation, and water depth. During the peak tidal flows, an accelerating high-speed zone occurred on the stoss (upstream) side of the hollow. Under the unstratified conditions, the momentum balance in the hollow is roughly achieved among barotropic pressure gradient, horizontal advection, and local acceleration. The form drag induced by the hollow was estimated to be one order of magnitude larger than the skin friction generated by bed roughness. The form drag can speed the upstream current in the hollow, forming the high-speed zone on the stoss slope of the hollow. Theoretical investigations reveal that a self-adjustment mechanism of the combined hollow and contraction can calm the channel flow speed, so that more flow energy can be dissipated and more river-derived sediments can be trapped within the estuary. This recognition implies that the Pearl River delta is estuarine accumulation dominated.
Proximate Composition, Trace and Macro Element, and Heavy Metal Content of Edible Seaweed Solieria robusta in Tawi-Tawi, Philippines

Solieria robusta is a red seaweed consumed widely by the local population in the southern Philippines. However, current information regarding its proximate composition, trace and macro element, and heavy metal content is lacking. This study marks the first attempt in the Philippines to ascertain the proximate composition, trace and macro element, and heavy metal content of edible seaweed S. robusta. Our findings revealed that S. robusta primarily consists of ash content, measuring 44.30 ± 0.4 g/100 g, followed by carbohydrates at 32.96 ± 0.45 g/100 g. The moisture content of dried S. robusta was 16.91±0.04 g/100 g. The crude protein content of this edible seaweed was determined to be 5.61 ± 0.05 g/100 g, with total fat noted at 0.22 ± 0.01 g/100 g. The average concentration of trace elements (Zn, Fe, Mg, and Cu), macro element (K), and heavy metals (Pb and Cd) followed the order of Zn > Fe > K > Mg > Pb > Cu > Cd. Notably, three essential minerals -Zn, Fe, and K- were present in significant quantities in S. robusta, with concentrations of 219±8.70, 209±5.75, and 9.83±0.22 mg/kg, respectively. However, one trace element, Zn, and one heavy metal, Pb, exceeded the permissible values, and the rest were within the safe limits. This study constitutes a significant contribution to the comprehension of the nutritional profile and mineral composition of S. robusta, emphasizing its potential as a valuable dietary resource.
A Real-World Data Derived Pharmacovigilance Assessment on Drug-Induced Nephropathy: Implication on Gaps in Patient Care

This retrospective cross-sectional study aims to investigate the prevalence and seriousness of drug-induced nephrotoxicity and to identify clinical predictors intensifying the seriousness of nephrotoxicity. Adverse drug events (ADEs) reported to the Korean Adverse Event Reporting System Database (KAERS DB) from January 2012 to December 2021 were investigated. The association between the seriousness and the etiologic drug was estimated in reporting odds ratio (ROR) based on disproportionality analysis. Logistic regression was utilized to recognize predictors associated with serious nephrotoxicity. The majority of ADEs were reported in ages 30 to 59, and immunosuppressants were the most etiologic medications. ADEs involving antibiotics, including vancomycin (ROR 0.268; 95% CI 0.129–0.557), were less likely to be serious. More than 93% of cyclosporine-related ADEs were serious nephrotoxicity, whereas tacrolimus was less likely to report serious nephrotoxicity (ROR 0.356; 95% CI 0.187–0.680). The risk of serious nephrotoxicity was decreased with aging (ROR 0.955; 95% CI 0.940–0.972) while increased in women (OR 2.700; 95% CI 1.450–5.008). Polypharmacy was associated with increased risk of interstitial nephritis (OR 1.019; 95% CI 1.001–1.038). However, further studies investigating the impact of clinical practice on ADE incidences as well as clinical prognosis related to nephrotoxicity are obligated.
Adaptive Finite-Time 6-DOF Tracking Control for Spacecraft Fly Around With Input Saturation and State Constraints

An adaptive finite-time control scheme is developed for noncooperative spacecraft fly-around subject to input saturation, full-state constraints, dynamic couplings, parameter uncertainties, and disturbances. Different from traditional fly-around model based on C–W equation, the derived 6-DOF spacecraft fly-around model can be suitable for noncooperative case in close proximity. By using the backstepping control technique, an integrated adaptive finite-time control law is designed, in which the tan-type barrier Lyapunov function (BLF) is incorporated to handle the full-state constraints. Meanwhile, the unknown dynamic couplings, parameter uncertainties, and disturbances are attenuated effectively by using adaptive estimation technique and the adverse effects raised from input saturation are reduced by the designed saturation compensator. Based on the constructed BLF, it is shown that the designed adaptive finite-time controller can guarantee that full-state constraints are not breached, but also can drive relative position and attitude tracking errors into the accurate convergent regions with finite-time convergence. Finally, the performance and advantage of the designed adaptive finite-time control scheme are demonstrated by numerical simulations.