This study aims to investigate the mechanical and morphological properties of hybrid composites fabricated from a Date Palm Mesh Fiber (DPMF) and glass wool reinforced with unsaturated polyester. The development of eco-friendly and efficient thermal insulation materials is crucial for reducing energy consumption and addressing environmental concerns. The hybrid composites were manufactured using the Bulk Molding Compound technique, and various factors such as fiber composition weight percentage, particle size, and quantities of DPMF and glass wool fibers were evaluated. Tensile, impact, and flexural bending tests were conducted to assess the mechanical properties of the composites. Design-Expert 12 software and analysis of variance ANOVA were employed to analyze the effects of fiber ratio, matrix ratio, and fiber size on the mechanical properties. The experimental results showed that the fiber content, DPMF content, and DPMF particle size in the matrix significantly influenced the mechanical properties of the hybrid composites. Increasing the fiber content and DPMF particle sizes improved the interfacial bonding between DPMF and the polymer matrix, enhancing the matrix's tensile strength and flexural strength of the composites. However, high amounts of DPMF resulted in poor energy absorption abilities of the composites under impact load. The fractography analysis using FESEM confirmed the mechanical test results by revealing a rough fracture surface in the composites reinforced with DPMF, indicating stronger bonding between the fibers and the unsaturated polyester matrix. This study highlights the potential of hybrid composites as eco-friendly and efficient thermal insulation materials and provides insights into the influence of various parameters on their mechanical properties.
Dry forests are ecosystems of great importance worldwide, but in recent decades they have been affected by climate change and changes in land use. In this study, we evaluated land use and land cover changes (LULC) in dry forests in Peru between 2017 and 2021 using Sentinel-2 images, and cloud processing with Machine Learning (ML) models. The results reported a mapping with accuracies above 85% with an increase in bare soil, urban areas and open dry forest, and reduction in the area of crops and dense dry forest. Protected natural areas lost 2.47% of their conserved surface area and the areas with the greatest degree of land use impact are located in the center and north of the study area. The study provides information that can help in the management of dry forests in northern Peru.
Aeromagnetic and Digital Elevation Model (DEM) data were analyzed to identify subsurface water-bearing zones and examine the topographical trends of surface and basement complex rocks in a portion of Kano State, Nigeria, bounded by latitudes 8°00'00''N to 9°00'00''N and longitudes 11°30'00''E to 12°30'00''E. The aeromagnetic data, sourced from the Nigerian Geological Survey Agency (NGSA), were subjected to filters, including Residual Magnetic Intensity (RMI) and Source Parameter Imaging (SPI), to estimate residual magnetic fields and depths to the basement complex rocks. The SPI results revealed two distinct depth classes: deeper and shallow regions. Deeper zones, characterized by depths ranging from 123.1 m to 414.4 m, were identified in the following areas: between 8°00'00''N and 8°38'24''N, 12°12'00''E to 12°30'00''E; 8°49'48''N to 9°00'00''N, 12°12'00''E to 12°30'00''E; 8°00'00''N to 8°07'12''N, 11°40'48''E to 12°00'00''E; 8°21'36''N to 8°37'48''N, 11°40'48''E to 12°00'00''E; 8°51'03''N to 9°00'00''N, 11°40'48''E to 12°00'00''E; and 8°14'24''N to 8°22'12''N, 11°33'36''E to 11°38'24''E. These regions, characterized by depression-like features, were suggested as optimal zones for groundwater exploration. The topographical analysis of the surface indicates that rainwater and leachates were transported toward the northern region of the study area, which exhibits relatively low elevations (448 m to 468 m above mean sea level). This region was identified as a likely accumulation area for surface water. The fresh basement complex rocks were observed to gently slope from south to north, with depth values ranging from 112.6 m to 117.7 m in deeper areas and 91.6 m to 109.8 m in shallower zones. The flow direction of surface water aligns with the underlying basement rock structure, suggesting that surface water runoff is likely influencing aquifer recharge processes. A cross-correlation coefficient of -0.99981 was observed between the surface and basement complex rock trends, indicating a strong inverse relationship between the two topographies. Consequently, the surface water accumulation zone was inferred to be a critical aquifer recharge area, though it may also facilitate the leaching of contaminants into the groundwater system, raising potential concerns for aquifer quality.
This paper investigates the search for an exact analytic solution to a temporal first-order differential equation that represents the number of customers in a non-stationary or time-varying $M / D / 1$ queueing system. Currently, the only known solution to this problem is through simulation. However, a study proposes a constant ratio, $\beta$ (Ismail's ratio), that relates the time-dependent mean arrival and mean service rates, offering an exact analytical solution. The stability dynamics of the time-varying $M / D / 1$ queueing system are then examined numerically in relation to time, $\beta$, and the queueing parameters. On another note, many potential queueing-theoretic applications to traffic management optimization are provided. The paper concludes with a summary, combined with open problems and future research pathways.
The rise of advanced digital technologies (ADT) within Industry 4.0 has transformed modern industrial operations, with select industry leaders emerging as pioneers in the integration of these technologies. This has positioned them as benchmarks for companies with limited digital capabilities. The vulnerabilities of Industry 4.0 to external disruptions, including natural disasters such as the earthquakes in Japan and Turkey, the COVID-19 pandemic, and especially the ongoing energy crises, exemplified by the war in Ukraine and sanctions on the Russian Federation, have necessitated a shift in business continuity management (BCM) strategies. Traditionally focused on safeguarding information technologies, BCM now places greater emphasis on ensuring energy independence and reducing reliance on state-controlled critical infrastructure. In response to these risks, enterprises are increasingly adopting resilient production models designed to restore functionality after cyberattacks, solar flares, extended power outages, and internet disruptions. The journey toward energy independence spans from initial recognition of the need for action to the implementation of robust solutions, such as Faraday cages for server protection and off-grid energy systems. While rare a decade ago, energy-independent enterprises are becoming more common, as illustrated by the copper smelter in Sevojno, a pioneering example. The acceleration of energy independence among companies has been driven by a series of crises, prompting significant BCM advancements. Early responses to these threats primarily focused on information technology (IT) disaster management methodologies, but Industry 4.0 discussions have evolved toward risk-resilient production systems. This study explores theoretical approaches to enhancing enterprise resilience to modern energy challenges, offering insight into emerging strategies aimed at safeguarding continuity in an increasingly volatile global landscape.
The establishment of a green campus relies on the adoption of green building, which involves reducing energy consumption, conserving water, managing waste effectively, and protecting natural resources. Numerous educational institutions in South Jakarta exceed 30 years of age. One such example is the Faculty of Engineering Building at Universitas Pancasila (Fakultas Teknik Universitas Pancasila/FTUP). FTUP has made an effort to implement green building practices to support the development of a green campus. The purpose of this research is to assess the implementation of green building at FTUP and to identify the barriers to its implementation. The assessment of green building implementation is carried out through a combination of interviews, observations, and archival analysis. Questionnaires were distributed to building managers and academic representatives at FTUP to identify the barriers to green building implementation. The data obtained was then analyzed using the RII (Relative Importance Index). The finding showed that the green building implementation at FTUP is low (32%), with the absence of a strategic plan as the main barrier. The results indicate that the identified barriers are not due to a lack of information or high costs.
This work continues the assessment of the application of carbon nanotubes (CNTs) mixed with zirconia (ZrO2). The study examined the compressive, bending, and bond strengths of samples containing and lacking carbon nanotubes. Zirconia carbon nanotubes (ZrO2) in the concentrations of 0.00 %, 0.01 %, 0.02 %, 0.03 %, 0.04 %, and 0.05 % were the subjects of six mixtures whose resistance was measured. The results were analyzed using the finite element method with the ANSYS 15.0 program. ANSYS 15.0 software is used to analyze compressive and bending loads as well as the conventional zirconia model. Showcase the advantages of moderately utilizing carbon nanotubes. Zirconia's mechanical properties can be improved more effectively by mineral/chemical mixtures or fibers without the issues related to carbon nanotube dispersion. Provide evidence of the advantages of moderately utilizing carbon nanotubes. Without the issues related to carbon nanotube dispersion or the health hazards of handling Nanomaterials, zirconia's mechanical properties can be improved more effectively by mineral/chemical mixtures or fibers. The maximum and ideal load for the load was found to be 163.5 MPa, which was approved in all tests after the six models were finished with their designs in the ANSYS program. This was based on the von mises stress value and the maximum shear stress value less than the yield strength of the basic material used. After making numerous attempts, this load was selected by increasing the load by a specific percentage until it reached the ideal load, at which point the original model was able to support the load without experiencing any problems. The results of the ANSYS program were compared and examined, and they showed that the models' resistance to deformations, displacements, stresses, and various strains greatly increased when carbon nanotubes were added. By adding more carbon nanotubes, those models will be more resilient to the strains and deformations caused by compressive loads. The deformation rate decreased by 60%, which was a very noticeable decrease, especially in the sixth model where the carbon percentage was 5%.
The dynamics of adaptive tourism sustainability in Hanjeli Tourism Village, Sukabumi Regency, Indonesia, were investigated to assess how local communities respond to tourism-induced transformations and how such responses influence economic resilience. A qualitative research design employing a case study approach was adopted to compare socioeconomic conditions prior to and following the village’s transition from a primarily agriculture- and mining-based economy to one centered on tourism. Historically reliant on subsistence farming, labor migration, and unregulated gold mining, the village has undergone a significant shift towards the cultivation of Hanjeli (Coix lacryma-jobi) and the implementation of educational tourism. Although initial resistance to tourism development was observed, a gradual adaptation was facilitated through the mobilization of endogenous resources, the implementation of community-based tourism (CBT), and the application of the sustainable livelihood framework (SLF). The involvement of stakeholders in homestay management, agro-tourism services, and local product development was found to significantly enhance economic resilience and reduce dependency on extractive and unsustainable income sources. The findings indicate that when adaptive strategies are rooted in local resource management and reinforced by active community engagement, long-term tourism sustainability becomes attainable. It is further suggested that policy frameworks should prioritize capacity-building programs and the diversification of economic activities to buffer against fluctuations in tourism demand, particularly under the influence of global uncertainties such as climate change and economic downturns.
This study investigates risk distribution models in the context of auto insurance in emerging markets, with a focus on the National Insurance Company (SAA), regional directorate of Setif, Algeria. The research applies generalized linear models (GLM) and factor analysis to model the frequency of vehicle accidents and their associated risks. A comprehensive approach is employed, beginning with a discussion of the techniques used for data collection and preliminary descriptive analysis. Following this, a theoretical framework is established for understanding the risk distribution models, highlighting the role of GLM in the modelling of accident frequencies within the insurance industry. Different types of factor analysis, including basic coefficient analysis, cross-factor analysis, generalized cross-factor analysis, and mixed factor analysis, are examined in relation to their applicability to insurance risk modelling. Subsequently, generalized linear models are implemented to derive a robust model for accident frequency, utilizing R software for analysis. The results reveal that the pricing system of the National Insurance Company is influenced by multiple, non-deterministic factors, which complicate the prediction of accident rates and insurance costs. These findings underscore the importance of incorporating various risk factors into pricing strategies, rather than relying on deterministic models. The study highlights the necessity of considering a broader range of factors in the development of pricing systems, particularly in emerging markets where data may be incomplete or subject to considerable variability. Furthermore, the use of Mixed Poisson models is suggested as an effective approach for capturing the non-linear relationship between various risk factors and accident occurrence. This research contributes to the existing body of knowledge by providing a nuanced understanding of the application of GLM and factor analysis in the auto insurance sector, particularly in emerging markets.
East Rembang is an area in the Rembang district prone to disasters. Various disaster management efforts have been carried out, but disaster risk measurement at the village level has yet to be done. Therefore, this research was carried out to know hazards, vulnerability, and coping capacity so that disaster risk in East Rembang can be analyzed. This research was carried out to know hazards, vulnerability, and coping capacity so that disaster risk in East Rembang can be analyzed. The research was conducted in East Rembang, consisting of three sub-districts, namely Kragan, Sarang, and Sluke, with 27, 23, and 14 villages, respectively. This research uses mixed methods, including literature study, interviews, and observation techniques. The research results show that the disaster threat and vulnerability of villages in East Rembang based on 2020-2022 data is, on average, low, although several villages have high threat and vulnerability. This is due to the relatively high capacity of the community, which supports low disaster risks. In general, it can be seen that the average level of disaster risk in East Rembang is “Very High”. Even so, the community's capacity to face disasters could be higher.
The mining sector plays a pivotal role in the economies of South Africa and Zimbabwe, yet limited attention has been given to the determinants of human capital disclosure within this industry. This study aims to address this gap by investigating the key factors influencing human capital reporting practices among the largest mining companies in these two countries. A quantitative approach was employed, utilising self-administered questionnaires to gather data from six major mining companies operating in both South Africa and Zimbabwe. Factor analysis was conducted to identify the primary determinants shaping human capital disclosure. The findings reveal that company structure, including audit committee characteristics, board size and composition, and assets, significantly influence disclosure practices. Performance-related factors, such as cost-effectiveness, return on training investments, liquidity, employee return on investments, and return on equity, also play a crucial role. Furthermore, market-related factors, including lobby pressure groups, media exposure, levels of debt, creditor pressure, and government regulations, were found to impact disclosure decisions. The results indicate that human capital disclosure mitigates information asymmetry, thereby strengthening relationships between company management and key stakeholders. It is also suggested that improved disclosure enhances corporate transparency, boosts investor confidence, and can positively influence a company’s perceived value. Given these findings, it is recommended that mining companies in South Africa and Zimbabwe adopt comprehensive reporting frameworks that incorporate human capital metrics. The adoption of such frameworks may align corporate practices with global reporting standards and enhance the sustainability and accountability of companies in the sector.
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.
Open Access
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