Nowadays, digital evidence plays a vital role in criminal investigations and arraignments. Digital criminal Investigators can also use this as an opportunity if the vast amount of data is a current trial. Assess constructive and constructive data and advice from the defendant proof behind the crime in terms of issues. Identifying criminal or criminal activity is a big deal because it connects certain data sets. It set an innovative law framework to quickly and accurately solve problems within the law's boundary. In this regard, the machine learning approach Naive Bayes classification for digital criminology data sets is to identify criminals. The Naive Baye classification process is used for digital criminology data application. To approximate square estimate for data sets of digital criminology subgroups. Also, support the Hadoop Big Data System Understanding Map with Reduce programming with the Naive Bayes classifier. The experiment result was a huge accumulated failure in the data quality. Based on these data, the estimation parameter of the statistical model is reached. The least-square estimate estimates the parameters that deal with the statistical model in the experimental result.
Rolling bearings play a critical role in various industrial applications. However, the complexity and diversity of data, along with the challenge of selecting the most representative features from a large set and reducing dimensionality to lower computational costs, pose significant challenges for accurately predicting the remaining useful life (RUL) of rolling bearings. To address this, a hybrid model combining the broad learning system (BLS) and multi-scale temporal convolutional network (MsTCN) is proposed for RUL prediction of rolling bearings. The BLS is employed to capture a broad range of features from the full-life signals of rolling bearings, while the MsTCN adaptively extracts multi-scale temporal features, effectively capturing both short-term and long-term dependencies in the bearing’s operational process. Additionally, the fusion and optimization of features extracted by BLS and MsTCN enhance the representational power of the prediction model. Experiments conducted on the PHM2012 bearing dataset demonstrate that the proposed method significantly improves model performance and prediction accuracy.
The pollution of water bodies by microplastic (MP) particles is a significant concern that has drawn the attention of environmental health organizations from various regions of the world. This concern is primarily caused by the potential of particles from the incomplete degradation of plastic waste to enter the food chain via water sources or fish consumed by humans. In Indonesia, the Krueng Aceh River is a water body that stretches across the Aceh Besar Regency and the Municipality of Banda Aceh (Indonesia). The river serves as a raw water source for clean water treatment for residents of both regions. The discovery of MP pollution in rivers in various regions of Indonesia as well as other countries has raised concerns regarding the presence of pollutants in the Krueng Aceh River. Therefore, this study identified MP particle pollution in the Krueng Aceh River water and assessed potential separation using ultrafiltration technology based on Polyethersulfone-graphene oxide membrane. Water samples were collected at five points along the river’s flow through the Aceh Besar area and Banda Aceh City. A total of 2 types of flat sheet membranes were created with a composition of Polyethersulfone polymer and graphene oxide in dimethylformamide. The ultrafiltration module was designed using cross-flow filtration with the feed of five samples of Krueng Aceh River water. Analysis was then conducted on the quantity, shape, and type of MP particles in water samples before and after ultrafiltration. The results showed that all water samples contained MP particles at a concentration of 18-22 particles/mL. This indicates that the Krueng Aceh River was already contaminated with MP pollutants. Therefore, special treatment efforts were needed by the government before it could be used as a source for the production of clean water for the residents of Banda Aceh City and Aceh Besar Regency. Based on these findings, the proposed alternative filtration technique can effectively remove pollutants by up to 91%.
Sustainable village development in West Sumatra Province has not been implemented optimally, so that many people are still below the poverty line. This can be seen from the Village Development Index for West Sumatra Province, where many villages are still very underdeveloped and disadvantaged. This study aims to analyze the influence of Village development characteristics in the perspective of sustainable development in West Sumatra Province. This research is a quantitative causal research. The population in this study was 846 villages in West Sumatra Province. Samples were taken using multistage sampling techniques with a total of 272 villages. Data was collected using a questionnaire. The data analysis applied was multiple regression. This study found that Institutional influence on sustainable village development (0.183); education level influences sustainable village development (0.777); community participation has an impact on sustainable village development (0.110); utilization of natural resources has an impact on sustainable village development (0.281) and poverty affects sustainable village development (-0.025).
This study aims to identify the most suitable deep learning model for early detection of dental caries in a new database of dental diseases. The study compares the performance of residual and dense networks using standard performance metrics. Dental caries is categorized into four classes based on dental practitioner recommendations. A novel database consisting of 1064 intraoral digital RGB images from 194 patients was collected in collaboration with Bharati Vidyapeeth’s Dental College, Pune. These images were cropped to obtain a total of 987 single-tooth images, which were divided into 888 training, 45 testing, and 54 validation images. In Phase I experimentation, ResNet50V2, ResNet101V2, ResNet152, DenseNet169, and DenseNet201 were utilized. Phase II focused on ResNet50V2, DenseNet169, and DenseNet201, while Phase III concentrated on DenseNet169 and DenseNet201. For Phase I experimentation, the overall accuracy of dental caries classification ranged from 0.55 to 0.84, with DenseNet exhibiting superior performance. In Phase II, the overall accuracy varied from 0.72 to 0.78, with DenseNet achieving the highest accuracy of 0.78. Similarly, in Phase III, DenseNet201 surpassed other models with an overall accuracy of 0.93. The DenseNet201 algorithm shows promise for detecting and classifying dental caries in digital RGB images. This finding is significant for the future development of automated mobile applications based on dental photographs, which could assist dental practitioners during examinations. Additionally, it could enhance patient understanding of dental caries severity, thereby promoting dental health awareness.
A two-month prospective study conducted at Hayatabad Medical Complex (HMC) Peshawar, Pakistan. In this study the pharmacotherapy patterns and drug-drug interaction (DDI) incidences were analyzed among 150 diabetic patients, of whom 50 presented with diabetic foot ulcer (DFU). Significant deviations from World Health Organization (WHO) core prescribing indicators were observed, particularly in the areas of polypharmacy and generic prescribing practices. The majority of DFU patients were from urban regions, with sedentary lifestyle factors identified as prominent contributors to DFU development. A higher incidence of DFU was noted among male patients with type 2 diabetes mellitus (T2DM) compared to female patients. Age distribution analysis revealed that patient ages ranged from 8 to 85 years, with 68% falling within the 41-60 age bracket, while only 2% were under 20 years of age. Among the all 391 pharmacotherapeutic agents prescribed, injectable medications constituted the majority (47.82%). Analysis of DDIs showed that 39.1% of prescribed medications were associated with drug interactions, with 72% of these classified as major interactions. The most frequently observed major DDIs involved combinations such as aspirin with Ramipril and Pregabalin with Losartan. These findings highlight the necessity for clinical pharmacists to review prescribing regimens to mitigate the risk of severe DDIs. The high prevalence of diabetes and DFU in this patient cohort is closely associated with lifestyle factors, insufficient health education, and lack of physical activity. These findings underline the urgent need for preventative strategies, including lifestyle modifications and public health education. Further investigation is recommended to enhance understanding of DFU risk factors and to develop improved prognostic and preventive frameworks.
Bentonite is a readily available natural clay that can be modified using simple and inexpensive procedures, making it a cost-effective option for removing various organic and inorganic contaminants. In this study, the raw bentonite (RB) is modified using acid and heat treatments. The physiochemical modification of bentonite (MB) was analyzed using the Buranner-Emmett-Teller (BET) technique to determine its surface area, and Fourier transformation infrared spectroscopy (FTIR) was used for further characterization. Post modification, bentonite has a better surface area from 35.15 m2/g for RB to 102.6 m2/g. The porosity of MB has also increased, offering more adsorption sites and overall enhanced surface properties. Fixed bed columns filled with MB and a mixture of MB and sand (MB+S) were used to investigate MB secondary wastewater purification capacity. Filtration was completed at a flow rate of 1 mL/min (a flow velocity of 3.1 cm/h) onto MB and MB+S mixture, respectively. The results show that the maximum removal efficiency for total suspended solids (TSS), turbidity, phenol (pH), chromium Cr⁶⁺, COD (Chemical Oxygen Demand), BOD5, total coliform (TC), fecal coliforms (FC), and electrical conductivity (EC) onto MB are 100, 100, 93.67, 90.43, 93.75, 97.78, 100, 100, and 30% respectively. The efficiency for these parameters is slightly reduced in modified bentonite (MB) and sand mixtures.
Lithium, as a critical resource underpinning strategic emerging industries, has garnered significant global attention due to its pivotal role in energy storage and clean energy applications. This study delineates the spatial economic network of China’s lithium industry by analysing data derived from lithium-related listed firms and their subsidiaries registered within the country. Employing social network analysis (SNA) and GeoDetector methods, the spatial characteristics and determinants of the economic network are systematically investigated. The findings reveal that lithium-related listed firms are predominantly concentrated in economically developed regions, including the Yangtze River Delta, Pearl River Delta, Hubei Province, and Henan Province. The economic network exhibits sparse connectivity but displays a small-world effect, characterised by a hierarchical structure with Shenzhen as the central hub, supported by significant nodes in Ningde and Shanghai. A distinct east-west disparity is observed, with dense linkages in the east contrasting with sparse connections in the west. Core cities within the network, primarily located in coastal regions, are identified as possessing strong economic development, favourable resource endowment, or well-established industrial foundations. These cities exhibit notable spatial agglomeration patterns around regional cores. Furthermore, the economic network is profoundly influenced by factors including economic development levels, local innovation capacity, openness to trade and investment, and policy environments conducive to industrial growth. These findings provide valuable insights into the spatial structure and driving mechanisms of China’s lithium industry, offering a robust basis for formulating targeted strategies to enhance the sector’s development and competitiveness.
An Integer Linear Programming (ILP) model was proposed to optimize academic timetables, with a focus on the School of Management at Western Galilee Academic College. The model was designed to address the dual challenges of course scheduling and faculty availability while incorporating a minor structural adjustment to enhance computational efficiency and accelerate convergence, particularly for large-scale problems. By employing this model, optimal scheduling solutions were generated within minutes, even in scenarios involving over 200 classes and 100 lecturers. The approach effectively minimizes planning time, identifies unavoidable scheduling conflicts, and highlights unschedulable classes due to constraint violations. Furthermore, the model provides actionable insights into staffing requirements, ensuring a comprehensive resource allocation strategy. Results from the application of the model during the 2023 winter semester demonstrated its capability to efficiently schedule 236 classes across multiple programs and instructional modalities. The method achieved adherence to predefined constraints, optimized the utilization of institutional resources, and enhanced overall scheduling efficiency. This case study underscores the potential of the proposed ILP framework to streamline academic timetabling processes, particularly in environments with diverse programmatic needs and complex resource interdependencies. The findings indicate that the model can be readily adapted to other academic institutions seeking to improve the effectiveness and precision of their scheduling systems.
This study investigates the structural performance and mass optimization of traditional walkers by comparing aluminum alloy and polymer matrix composites (PMCs) through advanced finite element analysis (FEA) using the ANSYS simulation platform. The FEA results reveal that peak stress, reaching 251.9 MPa, is concentrated at the front wheel support region, highlighting a critical area prone to structural vulnerability. Special attention is required to address potential mechanical limitations in key zones, such as the rear suspension, to prevent premature failure. Comparative analysis demonstrates that walkers fabricated from carbon-epoxy PMCs offer superior stiffness, reduced weight, and enhanced resistance to deformation compared to aluminum alloy counterparts. Notably, under descent conditions, the maximum elastic strain in the carbon-epoxy walker reaches 0.00399 mm/mm, localized in the front wheel support area, as indicated by the simulation results. These findings underscore the significant role of material selection in improving structural integrity and performance across varying operational conditions. The equivalence of stress and strain energy distributions further substantiates the advantages of composite materials over conventional alloys, suggesting that PMCs enable enhanced durability without compromising weight efficiency. The research emphasizes a human-centred approach, aligning material performance with user needs to develop mobility aids that offer long-term structural reliability. Beyond addressing immediate structural concerns, the findings lay the groundwork for future studies involving optimization algorithms and the exploration of alternative composites for assistive devices. The study provides valuable insights into stress distribution, deformation behaviour, and mechanical response, promoting continuous innovation in the design and development of mobility aids.
Returnee faculty play a pivotal role in international knowledge transfer and the advancement of scientific research within domestic universities. However, the effectiveness of returnees in enhancing institutional research performance remains inadequately understood. This study seeks to quantify and examine the collaborative academic networks of returnee faculty, assessing their impact on research output and funding performance. Based on an extensive academic dataset, comprehensive scientific collaboration networks (SCN) of returnees were constructed and empirically analyzed to elucidate the influence mechanisms underpinning research performance. The findings indicate that the presence of returnee faculty substantially enhances overall publication output and funding acquisition. Further, within the SCN of returnees, both academic influence and network expansion positively correlate with research productivity and funding success, whereas an increase in cooperation density appears to exert a negative effect. Additionally, the evolution of these collaboration networks was explored, revealing that returnees’ SCN display lower similarity and retention over time compared to those of native faculty. These insights offer a valuable theoretical basis for improving the management and integration of returnee faculty and optimizing the allocation of higher education resources, thereby fostering more effective pathways for enhancing institutional research outcomes.
This study investigates the regional logistics efficiency of Sichuan Province, China, from 2011 to 2019, using a combination of the Data Envelopment Analysis-Banker, Charnes, and Cooper (DEA-BCC) model and the Tobit model. The primary objective is to assess the efficiency of the logistics industry and identify the key determinants influencing this efficiency within the context of high-quality development. A comprehensive input-output index system and a set of influencing factor variables were constructed to evaluate logistics performance across various regions of the province. The findings indicate that factors such as the level of economic development, urbanization, and geographical location significantly enhance regional logistics efficiency. In contrast, the level of informatization and the industrial structure exhibit clear inhibitory effects. Specifically, a higher degree of informatization does not necessarily correspond with improved logistics efficiency, potentially due to inefficiencies in technology adoption or uneven infrastructure development. Furthermore, the current industrial structure, with its reliance on traditional industries, may hinder the optimization of logistics systems. Based on these results, several policy recommendations are put forward, including the optimization of the industrial structure, better integration of information technologies in logistics processes, and the strategic utilization of Sichuan’s geographical advantages. This research provides valuable insights for policymakers aiming to enhance logistics efficiency as part of the region’s broader economic development strategy.
Open Access
dwi putra darmawan 
, gede mekse korri arisena , ni made classia sukendar , ni luh made indah murdyani dewi , anak agung keswari krisnandika , putu perdana kusuma wiguna , dina lare dunensa , anak agung istri agung peradnya dewi , desak dwi asthri cahyani |
Available online: 09-29-2024
Farmer regeneration has been recognized as a critical factor in ensuring sustainable agricultural development and preserving ecological and cultural heritage. This study aims to: (i) examine the socio-demographic characteristics of rice farmers, (ii) assess the state of farmer regeneration, and (iii) analyze the labor requirements within the rice farming sector in West Denpasar District, Denpasar City, Bali, Indonesia. A mixed-method approach was employed, combining structured and in-depth interviews with 187 farmers, selected using the Slovin formula with a 5% margin of error and a 95% confidence level from a population of 352 farmers. Quantitative and qualitative data were collected through interviews and surveys to evaluate the agricultural system and workforce dynamics. The findings indicated that 59.36% of respondents identified farming as their primary occupation. However, a significant majority of their children pursued non-agricultural professions, citing the preference for stable income and professional careers. While most farmers endeavored to instill ecological values and emphasized the cultural significance of rice farming, 74.86% reported that their children had no engagement in agricultural activities. The labor force in rice farming primarily comprised family members supplemented by hired workers, particularly during labor-intensive periods. The employment of external workers was necessitated by extensive landholdings and the operational demands of mechanized and manual farming practices. These findings underscore the importance of addressing generational shifts in farming participation to ensure the sustainability of agricultural productivity and cultural heritage. Farmer regeneration was identified as pivotal to enhancing agricultural output, fostering ecological conservation, and improving community food security, while simultaneously addressing broader socio-economic challenges.
