Loop unrolling is a well-known code-transforming method that can enhance program efficiency during runtime. The fundamental advantage of unrolling a loop is that it frequently reduces the execution time of the unrolled loop when compared to the original loop. Choosing a large unroll factor might initially save execution time by reducing loop overhead and improving parallelism, but excessive unrolling can result in increased cache misses, register pressure, and memory inefficiencies, eventually slowing down the program. Therefore, identifying the optimal unroll factor is of essential importance. This paper introduces three ensemble-learning techniques—XGBoost, Random Forest (RF), and Bagging—for predicting the efficient unroll factor for specific programs. A dataset comprises various programs derived from many benchmarks, which are Polybench, Shootout, and other programs. More than 220 examples, drawn from 20 benchmark programs with different loop iterations, used to train three ensemble-learning methods. The unroll factor with the biggest reduction in program execution time is chosen to be added to the dataset, and ultimately it will be a candidate for the unseen programs. Our empirical results reveal that the XGBoost and RF methods outperform the Bagging algorithm, with a final accuracy of 99.56% in detecting the optimal unroll factor.

In the era of environmental crises and human challenges amidst rapid technological advancements, geography is an increasingly urgent discipline in comprehending the spatio-temporal dimensions of environmental sustainability. Therefore, effective, innovative, and collaborative implementation of geography learning in schools is essential. This goal can be achieved by emphasizing students’ critical thinking, problem-solving, and spatial thinking skills. The researcher designed an environmental problem-solving learning model to address this need. The environmental problem-solving learning model embraces problem-based learning focused on contextual environmental issues. This research aims to analyze the effectiveness of implementing the environmental problem-solving learning model with GIS-based learning media. The study employs an experimental design that utilizes a one-group pretest-posttest approach. The study group in this research was purposively selected, including 33 students from an urban area school, SMA Negeri 3 Semarang, and 35 students from a rural area school, SMA Negeri 1 Beringin. Data collection involved test methods, observations, and literature review. Qualitative data analysis was performed using an interactive method, while quantitative data analysis employed descriptive statistical analysis and a one-paired sample t-test. This research indicates that the environmental problem-solving learning model with GIS-based learning media effectively improves student learning outcomes. This model promotes active, student-centered learning, encourages collaboration and cooperation among students, and positions students as the primary subjects in the learning process. Furthermore, it fosters the development of critical thinking and problem-solving skills in students. The findings of this research underscore the potential of the environmental problem-solving learning model to be implemented in geography education. Various stakeholders play a crucial role as change agents in promoting innovative transformations in geography learning, including encouraging the realization of GIS-based environmental problem-solving models in various educational contexts.

Indonesia, a significant exporter of coffee, faces persistent challenges in accurately identifying and classifying coffee varieties based on aromatic characteristics, primarily due to the subjective variability of human sensory evaluation. To address these limitations, an electronic nose (e-nose) system was developed for the classification of coffee varieties through the analysis of aromatic profiles. The system integrates a DHT-22 sensor and four gas sensors (MQ-5, MQ-4, MQ-3, and MQ-135) to measure humidity, temperature, and gas concentrations from coffee vapor. Data acquisition was facilitated by the Arduino Uno platform, while classification was conducted using the Naive Bayes Classifier (NBC) algorithm. The e-nose achieved a classification accuracy of 82.2%, as validated through a confusion matrix and performance metrics, including precision, recall, and F1-score. Among the gas sensors employed, the MQ-4 sensor, which detects methane, demonstrated the highest response sensitivity, whereas the MQ-3 sensor, designed to detect alcohol, exhibited the lowest. This system significantly mitigates the inherent subjectivity associated with traditional aroma assessment methods and offers considerable potential for enhancing quality control protocols in coffee production processes. Future work will focus on integrating advanced machine-learning algorithms, optimizing sensor array performance, and expanding the dataset to include a broader diversity of coffee samples. These advancements are expected to further refine the system's classification capabilities and contribute to more robust quality assurance in the coffee industry.

Macroinvertebrate metrics are excellent tools for assessing water quality due to the sensitivity of biotic and abiotic parameters of their environment. The work aimed to assess the water quality of the Vilcanota River using aquatic macroinvertebrates and biological indices: Andean Biotic Index (ABI), Biological Monitoring Working Party (BMWP) score, and Ephemeroptera, Plecoptera, and Trichoptera (EPT) index. Macroinvertebrates were sampled at four sampling points (P1, P2, P3, and P4) during dry and wet seasons using Surber traps along a 600 m linear transect. In total, 1631 specimens belonging to 04 classes, 11 orders, and 24 families were found. The class Insecta presented the highest values with 1078 specimens (66.1%), six orders (54.5%), and 19 families (79.2%). The evaluation of the water quality of the Vilcanota River showed that the points during the wet season have questionable water quality for the BMWP and ABI indices. In the dry season, most sampling points (except P1, classified as questionable) showed critical water quality in both the BMWP and ABI index. Similarly, the ETP index revealed regular water quality in the wet season, while in the dry season was bad water quality for most sampling (except P2) points.

Soil salinization is a critical global issue that undermines agricultural productivity, threatens food security, and compromises the sustainability of farming systems. In the coastal region of Navotas, Balayan, Batangas, Philippines, the increasing salinization of irrigated rice farmlands due to saline-sodic soils has become a pressing concern. The understanding of farmers' perceptions regarding the risks associated with soil salinity, as well as their insights into the causes, consequences, and coping mechanisms, remains inadequately explored. This gap in knowledge may hinder the development of effective countermeasures and appropriate policy interventions. In this study, farmers' perceptions of saline-sodic soils were assessed through Key Informant Interviews (KIIs) and Focus Group Discussions (FGDs), which were analysed using thematic analysis and Causal Loop Diagrams (CLDs). Saltwater intrusion was consistently identified as the primary cause of soil salinization, with subsequent reductions in rice yields ranging from 60% to 100%, depending on the growth stage of the crop. The most vulnerable periods were identified as June to August, particularly during the vegetative and flowering stages of rice. Farmers reported coping strategies such as irrigation with freshwater and the cultivation of salt-tolerant rice varieties; however, challenges related to delayed access to freshwater and seeds were frequently mentioned. The expansion of saline-sodic areas was identified as the most significant threat to rice production, with the condition of saline-sodic soils in lowland rice paddies increasingly viewed as a worsening problem. These findings underscore the severe impact of saline-sodic soils on both agricultural productivity and farmers' livelihoods, providing valuable insights that can inform the development of tailored management strategies and mitigation measures.

Electromyographic (EMG) analysis was conducted to evaluate the functional characteristics of masticatory muscles in patients with myogenous temporomandibular disorders (TMD), aiming to enhance the clinical understanding of muscle activity in these conditions. Based on the Diagnostic Criteria for Temporomandibular Disorders (DC/TMD), 28 patients with myogenous TMD, characterized by persistent pain exceeding six months, were examined alongside a control group of 35 asymptomatic subjects. EMG assessments were performed on the masseter, temporalis, and suprahyoid muscles during resting states and maximum intercuspation clench. Quantitative parameters, including myoelectric indices in the amplitude domain and mean power frequency (MPF) in the frequency domain, were evaluated. Significant differences in muscle activity patterns between the TMD and control groups were observed. During maximum clenching, temporalis muscles (TA) in TMD patients exhibited a markedly higher asymmetry index and activity index, alongside a lower MPF, compared to the control group. Conversely, the MPF of the suprahyoid muscles was elevated, while masseter muscles (MM) displayed a reduction in MPF. In the resting state, the MPF of the TA was found to be higher than that of both the control group and the MM. These findings indicate that patients with myogenous TMD exhibit increased muscle activity asymmetry, reduced coordination, and altered frequency-domain characteristics of the masticatory muscles. The results suggest that the TA may play a more significant role in the compensatory mechanisms associated with myogenous TMD, potentially contributing to the observed dysfunction and pain. This study underscores the utility of EMG as a diagnostic tool for elucidating the pathophysiological changes in masticatory muscle function in TMD and highlights the potential for targeted therapeutic interventions based on these findings.

The WBM’s viscosity, gel strength and ability to filter and control the filtration rate are central to the stabilization of the well bore as well as the transportation of the cuttings to the surface. WBM is comprehensively inexpensive and eco-friendly; it does not hinder the biodegradation process as compared to other chemicals that may be used in the drilling involving rig activities. It cans thermally change, regulate formation pressure, and support cuttings. WBM is also can be used in all types of formations and is not complicated in terms of its handling as well as disposing as compared to other drilling fluids. However, WBM has some limitations as it is influenced by shale hydration, formation water salinity and thermally less stable at high temperature formations. Hindered by formation solids, fluid loss to the formation and formation damage are other issues that must be dealt with efficiently during the drilling process using WBM. WBM is used in most practices of drilling especially in offshore drilling areas, environment sensitive areas, and areas that have certain restrictions on the types of fluids to be used in drilling. Effectiveness and flexibility in relation to various platforms and various rigs make it a prime candidate for the most orthodox as well as the most innovative operations. Some of the regular water based muds that are often in use are Spud mud, Low solid polymer mud KCl, PHPA polymer mud KCl, Glycol polymer mud, Salt Saturated mud and Drill – in mud.

The global iron and steel sector is currently navigating a period marked by significant volatility, driven by rising overcapacity and stagnating demand. In this challenging environment, businesses are increasingly compelled to compete not only within their local markets but also on the international stage, as the global economy becomes ever more interconnected. This necessitates a thorough evaluation of the financial performance of major firms in the iron and steel industry, particularly those listed on the Borsa İstanbul (BIST). Such assessments are critical for informing strategic decision-making within the sector. This study aims to assess the financial performance of prominent iron and steel companies traded on BIST between 2019 and 2023, employing an advanced multi-criteria decision-making (MCDM) approach. Specifically, an Improved ENTROPY method is combined with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank the fiscal performance of these enterprises. The findings indicate that EREGL stands out as the highest-performing company in terms of financial metrics over the specified period. The study offers valuable insights into the financial health and operational efficiency of iron and steel firms, providing key information for investors and policymakers in the sector. Additionally, the proposed methodology presents a robust framework for the evaluation of corporate performance in other industries facing similar global challenges.