Optimal Monitoring Research Articles

Management of Fixed Asset Management at the Manpower and Transmigration Service of Penukal Abab Lematang Ilir Regency (PALI)

Assets are the component with the greatest value and wealth that is vital for the running of an organization, whether in a government or private company. Researchers will focus this research on the Manpower and Transmigration Service of PALI Regency and how fixed asset management works. whether the implementation of asset management is in accordance with established regulations. So far, management of regional inventory items has been carried out on the basis of the provisions of Minister of Home Affairs Regulation Number 7 of 1997 as the main regulations for regional government inventory regulations. The aim of this research is to understand how fixed asset management at the PALI Regency Manpower and Transmigration Service works. This research uses qualitative research methods with an emphasis on Doli D. Siregar's theory of asset indicators, namely: asset inventory, legal audit, asset assessment, asset optimization, and asset monitoring and control. In selecting informants, researchers used purposive. The techniques used by researchers in collecting data are interviews, observation, and documentation studies. The conclusion from the results of this research shows that fixed asset management at the Manpower and Transmigration Department is not running well. This is because there is still a lack of human resources in the field of asset management, one of whose tasks is to inventory assets, and the large number of assets makes asset managers overwhelmed.

Validation of schedules for optimal prostate-specific antigen monitoring after radical prostatectomy.

Early detection of biochemical recurrence (BCR) after radical prostatectomy (RP) is crucial for early treatment and improving survival outcomes. The optimal prostate-specific antigen (PSA) monitoring remains unclear, and several models have been proposed. We aimed to externally validate four models for optimal PSA monitoring after RP and propose modifications to improve them. We reviewed the clinicopathological data of 896 patients who underwent robot-assisted RP between 2009 and 2022. We examined all PSA values and estimated the PSA value for four monitoring schedules at each time point in the virtual follow-up. We defined the ideal PSA for BCR detection between 0.2 and 0.4 ng/mL. During the median follow-up of 21.4 months, 128 (14.3%) patients presented BCR. The original and modified Keio models, National Cancer Center Hospital model, and American Urological Association/American Society for Radiation Oncology model detected BCR in 14 (10.9%), three (2.3%), 12 (9.4%), and 11 (8.6%) patients with PSA >0.4 ng/mL. Most patients experienced BCR detected with PSA >0.4 ng/mL during the first year postoperative. The modification of interval within 6 months postoperative avoided BCR detection with PSA >0.4 ng/mL within the first year postoperative in 8/9 (88.9%), 1/2 (50.0%), 5/6 (83.3%), and 4/4 (100%) for the original and modified Keio models, National Cancer Center Hospital model, and American Urological Association/American Society for Radiation Oncology model, respectively. We validated four models for PSA monitoring after RP to detect BCR and suggested modifications to avoid detections out of the desired range of PSA. These modifications could help to establish an optimal PSA monitoring schedule after RP.

Исследование проблематики методик определения типа контента во входящем трафике

Content filtering in the context of cybersecurity and trusted environments is an important tactic used to ensure network security and functionality. It works by restricting access to certain websites, emails, files or other content that may contain harmful elements or pose a significant risk of infection. Content filtering ensures the security not only of an individual user's data, but also of an entire network of organizations and institutions, helping to minimize the risk of malicious security breaches. The study of methods for determining the type of content in incoming traffic is a relevant and important area in the field of information security and network analytics. In today's Internet space, a significant amount of data is transmitted through networks, and one of the key tasks is the classification of this traffic to ensure security and effective network management. Methods for determining the type of content in incoming traffic are a set of algorithms and approaches that allow you to automatically determine what type of data is transmitted over the network. In the course of studying the problems of methods for determining the type of content in incoming traffic, data on network traffic is collected, a data set is selected for training the model, we consider classifier algorithms and focus on metrics for assessing classification efficiency. The results of the study can be used to create effective systems for detecting malicious or unwanted content, filtering data, or optimizing the operation of network resources. The study of methods for determining the type of content in incoming traffic is of practical importance and can be applied in various fields, including information security, network analytics, monitoring of network resources and optimization of network processes.

Inline Bondwave Monitoring for Direct Bonding, Process Optimization and Impact on Post-Bond Distortion

Direct wafer bonding, named also fusion wafer bonding, is based on the formation of molecular bonds between the two surfaces placed in contact. By the initiation of the contact between the two surfaces the bond is self-propagating: at the initiation point a bondwave is generated, which propagates free until the entire surfaces are in contact (bonded). The dynamic of the bondwave propagation is depending on various material (substrates) and process parameters. As shown by Rieutord et al. (1), and experimentally confirmed (2), for a given material and geometry, the speed is function of adhesion energy and fluid viscosity. For industrial applications, this means that investigating the bondwave speed by inline monitoring can be a way to monitor adhesion energy. Adhesion energy itself is a function of different parameters such as surface roughness, WET treatment or water presence (2,3).Inline IR monitoring can be also a practical solution for defect detection. Compared to post bond IR inspection, the live information allows for easier root cause analysis, e.g., particle defect, surface inhomogeneity or chuck effect. Inline early detection of defective bonds and process outliers enables bonded pairs to be reworked (separated and re-bonded) without any additional metrology steps such as post bonding IR inspection or Scanning Acoustic Microscopy (SAM). In case of fast bondwave, one has to monitor any unwanted effect such as edge voids or increased distortion (4).In an automated fusion bonding equipment, EVG®850LT, a bonding chamber has been equipped with an IR camera to record the bondwave propagation. The chamber has also connected different gas and vent lines and a humidifier to control the bonding atmosphere. The bond is initiated with a mobile pin equipped with a load cell, allowing to start the bondwave in a repeatable way at different locations (each bond must be initiated in a single point, but location might vary between center or edge proximity). As shown in figure 1 for a 300 mm Si-SiO2 bonded pair, spatial information can be extracted from the IR images, allowing for bondwave speed mapping (5). The equipment is also equipped with a plasma chamber for pre-processing. This allows to evaluate, using inline bondwave observation, various process conditions such as: plasma treatment, bond chamber gas type, pressure, humidity and bond pin location. Moreover, a metrology equipment was used for bonded pair shape measurement. This data is used to predict high resolution distortion maps to monitor the effect of bonding wave speed and bonding parameters.Different use cases of bondwave monitoring for process development will be presented, as well as the influence of different process parameters on bondwave propagation. A detailed analysis of the impact of the bond wave speed on bonded stack distortion will be presented. As shown in figure 1 (a) and (b), bonding wave velocity has no impact on high order distortion but will significantly increase the runout or scaling of the bonding pair.1. F. Rieutord, B. Bataillou, and H. Moriceau, Phys. Rev. Lett., 94, 236101 (2005).2. V. Larrey, G. Mauguen, F. Fournel, D. Radisson, F. Rieutord, C. Morales, C. Bridoux, and H. Moriceau, ECS Trans., 75, 145 (2016).3. D. Radisson, Phd thesis, Université Grenoble Alpes, (2014).4. M. Broekaart, A. Castex, K. Landry, R. Fontaniere, and C. Lagahe-Blanchard, ECS Trans., 50, 371 (2013).5. Fournel, F, B Rousset, V Larrey, C Morales, R Sachs, L Sudrie, and C Morvan, WaferBond’22 Proceeding, 2022. Figure 1

High-frequency data significantly enhances the prediction ability of point and interval estimation

Accurate prediction of dissolved oxygen (DO) dynamics is crucial for understanding the influence of environmental factors on the stability of aquatic ecosystem. However, limited research has been conducted to determine the optimal frequency of water quality monitoring that ensures continuous assessment of water health while minimizing costs. To address these challenges, the present study developed a hybrid stochastic hydrological model (i.e., ARIMA-GARCH hybrid model) and machine learning (ML) models. The objective of this study is to identify the best-performing model and establish the optimal monitoring frequency. Results revealed that high-frequency DO monitoring data exhibit greater variability compared to low-frequency data. Moreover, the ARIMA-GARCH model demonstrates promising potential in predicting DO concentrations for low-frequency monitoring data, surpassing ML models in performance. Furthermore, increasing the monitoring frequency significantly improves the prediction accuracy of models, regardless of whether point (with lower R2 values of 0.64 and 0.51 for daily detection than these of every 15 min (0.96 and 0.99) at CHQ and LHT, respectively) or interval predictions (with RIW higher values of 2.00 and 1.55 for daily detection higher than these of 0.02 and 0.16 in every 15 min at CHQ and LHT, respectively) are considered. Additionally, a 4 hourly monitoring frequency was found to be optimal for water quality assessment using each model. These findings identify the superior performing of the ARIMA-GARCH model and highlight the crucial role of monitoring frequency in enhancing DO prediction and improving model performance.

Towards Smart Sanitation: A Comprehensive Survey of IoT, AI, and ML in Public Toilet Management

This research paper presents a comprehensive review of literature surveys about the implementation of diverse technological approaches, including the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML), in the domain of public toilet monitoring systems. The study investigates the current landscape of monitoring technologies applied to public toilets, aiming to identify trends, challenges, and opportunities associated with each technique. Through an in-depth analysis of various literature surveys, the paper synthesizes the state-of-the-art advancements in IoT, AI, and ML as applied to public toilets, offering a nuanced understanding of the potential impact on hygiene maintenance, resource optimization, and real-time monitoring. The findings of this review contribute valuable insights for researchers, policymakers, and practitioners seeking to enhance the efficiency and sustainability of public toilet facilities.

Comprehensive modeling of cell culture profile using Raman spectroscopy and machine learning

Chinese hamster ovary (CHO) cells are widely utilized in the production of antibody drugs. To ensure the production of large quantities of antibodies that meet the required specifications, it is crucial to monitor and control the levels of metabolites comprehensively during CHO cell culture. In recent years, continuous analysis methods employing on-line/in-line techniques using Raman spectroscopy have attracted attention. While these analytical methods can nondestructively monitor culture data, constructing a highly accurate measurement model for numerous components is time-consuming, making it challenging to implement in the rapid research and development of pharmaceutical manufacturing processes. In this study, we developed a comprehensive, simple, and automated method for constructing a Raman model of various components measured by LC–MS and other techniques using machine learning with Python. Preprocessing and spectral-range optimization of data for model construction (partial least square (PLS) regression) were automated and accelerated using Bayes optimization. Subsequently, models were constructed for each component using various model construction techniques, including linear regression, ridge regression, XGBoost, and neural network. This enabled the model accuracy to be improved compared with PLS regression. This automated approach allows continuous monitoring of various parameters for over 100 components, facilitating process optimization and process monitoring of CHO cells.

A novel DT-based intelligent experiment method for complex industrial products

To alleviate the contradiction between multi-disciplinary coupling, high intelligence, and the difficulty in conducting direct experiments for product testing in the industrial field, a new method of intelligent experiment is proposed based on the digital twin. This method exhibits typical characteristics such as the joint integration of knowledge and data and the fusion of virtual and physical elements. The intelligent experiment technology framework suitable for complex industrial product experiment is established through a four-step intelligent experiment transformation method in the industrial field. These steps include a laboratory prototype experiment, a virtual simulation experiment, a hardware-in-the-loop experiment, and a virtual-physical fusion experiment. Aiming to address three typical engineering problems related to position and posture, posture and performance coupling, and micro force testing, three knowledge and data joint driving methods based on Virtual Reality (VR) multidisciplinary fusion calculations are proposed. A multi-agent operation scene is created, which maintains shape, space–time, physical behavior, performance, and macro–micro consistencies. Design and monitoring experiments are completed to achieve the co-evolution and closed-loop feedback between the physical and virtual dimensions. Taking the hydraulic support used in coal mines as an example, a high confidence agent model is established based on the single-machine hydraulic support posture monitoring and loading test bench. Through a virtual and physical cooperative operation mode involving multi-agents and physical models, six types of experiments are designed, including pose, force, and monitoring. Relevant experimental research shows that the requirements of industrial experiments can be met under laboratory conditions. This experiment framework can provide reliable experimental support for the optimization and intelligent monitoring of complex industrial products.

An optimized development of urban air quality monitoring network design based on particulate matters.

The design of an air quality monitoring network (AQMN) is the mandatory step to manage air pollution in megacities. Several studies are being done on the location selection of AQMNs based on topography, meteorology, and pollution density. Still, the critical research gap that needs to be addressed is the role of pollutants' importance and prioritization in AQMN. This study aims to utilize the sphere of influence (SOI) method to design an AQMN in a megacity based on particulate matter (PM) as the most serious urban pollutant. Model evaluation was done by employing annual emission inventory data of PM in Tabriz, an industrial and crowded megacity with high exposure to salt particulates, considering 3549 square blocks with a size of 500m * 500m. Then, the SOI methodology utilizing the utility function (UF) approach is applied using MATLAB software calculations to determine optimal air quality monitoring network configurations. A range of numbers of utility functions was yielded for every spot on the map. It resulted in grid city maps with final spots for PM10, PM2.5, and intersecting spots. As a result, ten sites are selected as the best possible locations for the AQMN of a 2 million population city. These results could play a precise and significant role in urban air quality decision-making and management.

Introducing next-generation transcranial surgery with the head-mounted 3D View Vision display in extracorporeal microsurgery: illustrative cases.

Exoscopy in neurosurgery offers various advantages, including increased freedom of the viewing axis while the surgeon maintains a comfortable upright position. However, the optimal monitor positioning to avoid interference with surgical manipulation remains unresolved. Herein, the authors describe two cases in which a three-dimensional head-mounted display (3D-HMD) was introduced into a transcranial neurosurgical procedure using an exoscope. Case 1 was a 50-year-old man who presented with recurrent epistaxis and was diagnosed with an olfactory neuroblastoma that extended from the nasal cavity to the anterior cranial base and infiltrated the right anterior cranial fossa. Case 2 was a 65-year-old man who presented with epistaxis and was diagnosed with a left-sided olfactory neuroblastoma. In both cases, en bloc tumor resection was successfully performed via a simultaneous exoscopic transcranial approach using a 3D-HMD and an endoscopic endonasal approach, eliminating the need to watch a large monitor beside the patient. This is the first report of using a 3D-HMD in transcranial surgery. The 3D-HMD effectively addressed issues with the field of vision and concentration while preserving the effectiveness of traditional microscopic and exoscopic procedures when observed on a 3D monitor. Combining the 3D-HMD with an exoscope holds the potential to become a next-generation surgical approach.

Quality of Service at Public Health Center in West Pasaman District in 2023

Background: The quality of health services is essential and is a concern at all levels of health services globally. The Public Health Center (PHC) was accredited, but poor service quality performance still needed to be improved. The research aims to analyze and explore the quality of health services. This research was descriptive qualitative. Methods: This research uses a phenomenological design study. Qualitative research was conducted with in-depth interviews, observation, and document review. This research was carried out from March to July 2023. There were eight informants: the Head and staff of the PHC and staff from the West Pasaman Health Office. Results: There needs to be more human resources, infrastructure, health equipment, and budget support, less optimal monitoring and evaluation, and no officer satisfaction survey has been conducted. Conclusion: PHC needs additional human resources (pharmacists and dentists), building improvements, complete health infrastructure, and equipment, as well as monitoring and evaluation, are required. The PHC needs to improve the quality of health services through the Plan-Do-Check-Action and Plan-Do-Study-Action cycles to achieve continuous quality improvement. Keywords: Public Health Centers, Service Quality, Quality Improvement

Serious Adverse Events and Laboratory Monitoring Regimens for Outpatient Parenteral Antimicrobial Therapy With Cefazolin and Ceftriaxone.

The optimal laboratory monitoring frequency for outpatient parenteral antimicrobial therapy-related adverse events (OPAT-AEs) during cefazolin and ceftriaxone therapy is not well defined. We identified 2.7 OPAT-AEs per 1000 sets of weekly laboratory tests in this population, suggesting that less intensive laboratory monitoring may be safe and reasonable.

High Hydroxyurea Usage in Sickle Cell Anemia Regardless of Patient Demographic

Introduction/Background: Hydroxyurea is highly effective in sickle cell disease, yet it is still underutilized. The Pediatric Sickle Cell Program of Northern Virginia recently published a 10-year analysis of uniform hydroxyurea (HU) implementation starting in infancy for all patients with sickle cell disease of severe genotype (SCA), demonstrating rise in HU usage from 32.5% baseline rate to 79.6% after two years, and to > 90% after 8 years. 1 Other HU utilization studies reporting lower rates largely use Medicaid data, and lower socioeconomics is often cited as a barrier to HU utilization. The Northern Virginia study reported on all patients with SCA regardless of insurance coverage, thus raising the question whether the high HU usage rate achieved in this program was influenced by a high proportion of non-Medicaid patients or other unusual demographic features. Therefore, we analyzed 10 years of HU usage data by insurance, ethnicity, and age. Methods: HUdata extracted from a longitudinal REDCap database and electronic medical records (EMR) with IRB approval (Children's National Medical Center IRB, Pro00002745, Pro00013374) previously reported 1 for five 2-year intervals are additionally analyzed for insurance, ethnicity, and age, with new data for 2019-2021. HU-eligibility includes SCA patients age ≥ 9 months and not chronically transfused. HU users (on HU) are patients demonstrating Hgb F rise and expected hemogram changes in response to HU, monitored every 2-3 months; HU non-users (not on HU) are patients who refuse HU or are grossly non-adherent as evidenced by lack of changes in Hgb F and hemogram. Pharmacy dispense records available in Epic EMR are used as corroborative information. Results: 1. Medicaid patients comprised 39 - 46% and non-Medicaid patients comprised 54 - 61% of all patients over the five 2-year periods. African-American (AA) and African patients comprised 85% of all patients. Medicaid coverage ranged from 24 - 39% for AA patients and 38 - 48% for African patients. (African is defined as born in Africa or having parents born in Africa who immigrated to the US.) 2. Comparing HU usage by insurance over the five 2-year periods, HU users increased from 72% to 91% for Medicaid patients and from 83% to 95% for non-Medicaid patients. Conversely, HU non-users decreased from 28% to 9% for Medicaid patients and from 17% to 5% for non-Medicaid patients. No statistical difference was detected in HU usage by Medicaid versus non-Medicaid insurance in any interval (p = 0.13 to 0.91) (Table 1). 3. HU usage rates in AA and African patients were not statistically different (p = 0.07 to 0.32), except in one interval (2013-15) (p = 0.006) (Table 1). 4. Comparison of HU usage in two age groups, 9 months-12 years and ≥ 13 years, showed that while HU non-acceptance/non-adherence tended to be higher in the age ≥ 13 group, no statistical significance was reached in any interval (p = 0.13 to 0.97) (Table 1). 5. The uniform HU implementation program targeted for fetal hemoglobin (Hgb F) 30% or higher by frequent HU dose optimization and close adherence monitoring. Hgb F > 20% was considered good adherence, and Hgb F 10-20% reflected partial adherence. Partially adherent patients did not distribute differently between Medicaid and non-Medicaid insurance (p = 0.07 to 0.86) but were predominantly age > 13 (p = 0.0005 to 0.001) (Table 2). Conclusion: 1. HU users and HU non-users did not disproportionately distribute to any demographic group. 2. Uniform HU implementation was effective in both Medicaid and non-Medicaid populations. 3. Ethnicity and age were not significant factors in HU uptake or acceptance, but decreased HU adherence is more frequent in teenagers. 4. HU can be effectively implemented for all SCA patients across demographic groups. Medicaid insurance, as a proxy for socioeconomic level, is not a barrier to high HU usage. Reference: 1. Phan V, Park JA, Dulman R, Lewis A, Briere N, Notarangelo B, Yang E. Ten-year longitudinal analysis of hydroxyurea implementation in a pediatric sickle cell program. Eur J Haematol. 2022 Nov;109(5):465-473. doi: 10.1111/ejh.13827. Epub 2022 Sep 2. PMID: 35811388.

IoT Monitoring for PV System Optimization in Hospital Environment Application

Purpose: The objective of this study is to implement IoT monitoring to remotely monitor PV system output and efficiency to ensure the continuous supply to ventilators and monitors in the ICU Room of RSI Siti Khadijah Palembang.
 Methodology/approach: The approach implemented in this study is by installing IoT Monitoring as an automatic transfer switch to ensure the continuous supply for the load.
 Results/findings: The IoT monitoring shows the real-time production of PV panels, battery capacity, and inverter output. Hence, the operator can monitor the PV system output and decide whether to keep using sources from PV panels or switch to grid utilities during cloudy/rainy days.
 Limitations: The IoT monitoring system has supervised the system's reliability, but it is necessary to improve the output and efficiency of the PV system by adding artificial intelligence algorithms and installing active cooling to prevent overheating.
 Contribution: This study shows the effectiveness of implementing IoT monitoring for the On-Grid PV system installed on the rooftop of RSI Siti Khadijah Palembang.

A parsimonious approach to predict regions affected by sewer-borne contaminants in urban aquifers

Leaky urban drainage networks (UDNs) exfiltrating wastewater can contaminate aquifers. Detailed knowledge on spatiotemporal distributions of water-dissolved, sewer-borne contaminants in groundwater is essential to protect urban aquifers and to optimize monitoring systems. We evaluated the effect of UDN layouts on the spreading of sewer-borne contaminants in groundwater using a parsimonious approach. Due to the UDN’s long-term leakage behavior and the existence of non-degradable sewer-borne contaminants (equivalent to a conservative and constant contaminant source), we employed a concept of horizontal line sources to mimic the UDN layout. This does not require the consideration of bio-degradation processes or temporal delay and effectively bypasses the vadose zone, thus reducing computational requirements associated with a full simulation of leakages. We used a set of synthetic leakage scenarios which were generated using fractals and are based on a real-world UDN layout. We investigated the effects of typical leakage rates, varying groundwater flow directions, and UDN’s layouts on the shape of the contaminant plume, disregarding the resulted concentration. Leakage rates showed minimal effects on the total covered plume area, whereas 89% of the variance of the plume’s geometry is explained by both the UDN’s layout (e.g., length and level of complexity) and groundwater flow direction. We demonstrated the potential of applying this approach to identify possible locations of groundwater observation wells using a real UDN layout. This straightforward and parsimonious method can serve as an initial step to strategically identify optimal monitoring systems locations within urban aquifers, and to improve sewer asset management at city scale.

Atrial Fibrillation Detection and Load: Knowledge Gaps Related to Stroke Prevention.

Atrial fibrillation is a major cause of ischemic stroke. Technological advances now support prolonged cardiac rhythm monitoring using either surface electrodes or insertable cardiac monitors. Four major randomized controlled trials show that prolonged cardiac monitoring detects subclinical paroxysmal atrial fibrillation in 9% to 16% of patients with ischemic stroke, including in patients with potential alternative causes such as large artery disease or small vessel occlusion; however, the optimal monitoring strategy, including the target patient population and the monitoring device (whether to use an event monitor, insertable cardiac monitor, or stepped approach) has not been well defined. Furthermore, the clinical significance of very short duration paroxysmal atrial fibrillation remains controversial. The relevance of the duration of monitoring, burden of device-detected atrial fibrillation, and its proximity to the acute ischemic stroke will require more research to define the most effective methods for stroke prevention in this patient population.

Expectations related to home-based telemonitoring of high-risk pregnancies: A qualitative study addressing healthcare providers' and users' views in Norway.

A pregnancy can be evaluated as high-risk for the woman and/or the fetus based on medical history and on previous or ongoing pregnancy characteristics. Monitoring high-risk pregnancies is crucial for early detection of alarming features, enabling timely intervention to ensure optimal maternal and fetal health outcomes. Home-based telemonitoring (HBTM) is a marginally exploited opportunity in antenatal care. The aim of this study was to illuminate healthcare providers' and users' expectations and views about HBTM of maternal and fetal health in high-risk pregnancies before implementation. To address diverse perspectives regarding HBTM of high-risk pregnancies, four different groups of experienced healthcare providers or users were interviewed (n = 21). Focus group interviews were conducted separately with midwives, obstetricians, and women who had previously experienced stillbirth. Six individual interviews were conducted with hospitalized women with ongoing high-risk pregnancies, representing potential candidates for HBTM. None of the participants had any previous experience with HBTM of pregnancies. The study is embedded in a social constructivist research paradigm. Interviews were analyzed using a thematic approach. The participants acknowledged the benefits and potentials of more active roles for both care recipients and providers in HBTM. Concerns were clearly addressed and articulated in the following themes: eligibility and ability of women, availability of midwives and obstetricians, empowerment and patient safety, and shared responsibility. All groups problematized issues crucial to maintaining a sense of safety for care recipients, and healthcare providers also addressed issues related to maintaining a sense of safety also for the care providers. Conditions for HBTM were understood in terms of optimal personalized training, individual assessment of eligibility, and empowerment of an active patient role. These conditions were linked to the importance of competent and experienced midwives and obstetricians operating the monitoring, as well as the availability and continuity of care provision. Maintenance of safety in HBTM in high-risk pregnancies was crucial, particularly so insituations involving emerging acute health issues. HBTM requires new, proactive roles among midwives, obstetricians, and monitored women, introducing a fine-tuned balance between personalized and standardized care to provide safe, optimal monitoring of high-risk pregnancies.

PENGELOLAAN DANA DESA DALAM MENINGKATKAN PEMBANGUNAN NON FISIK DI DESA PALIAT KECAMATAN KELUA KABUPATEN TABALONG


 
 
 
 Village Fund Management in Paliat Village, Kelua District, Tabalong Regency in order to achieve development goals, especially in non-physical development effectively and efficiently. With the existence of the Village Fund, the opportunity for development is greater in this case to fulfill the wishes of the community. From the results of observations there are problems, namely the lack of public understanding of transparency or budget reports from the Village Government, lack of community participation in the implementation of non-physical development, and the Not Optimal Process of Implementing Non-physical Development. The purpose of this researcher is to find out the management of Village Funds in increasing non- physical development in Paliat Village, Kelua District, Tabalong Regency and the influencing factors. This study used a qualitative approach with a qualitative descriptive type. The data collection techniques used were interviews, observation and documentation. The data source was taken through purposive sampling of 13 people. After the data was collected, it was analyzed using techniques including data condensation, data presentation and extension of observations, trigulation, analysis of negative cases, and conducting member checks .The results of the study showed that the management of village funds in Paliat Village, District the two districts of Tabalong Regency is are still not good. Good management can be seen from the planning aspect by formulating activities, the organizing aspect, assigning tasks, and grouping activities from the aspect of activating the division of labor for each. what is desired, the aspect of organizing how to collect human resources from the aspect of mobilizing there is a way to optimize human resources and from the aspect of supervision seen from monitoring and evaluation of performance Factors that influence inhibiting factors management is limited budget funds and less optimal monitoring activities. And the supporting factors for management are good planning by holding village development planning meetings and good division of tasks in an activity, which is under special coordination from the Head of Village Services. In order to improve the management of Village Funds in increasing non-physical development in Paliat Village, Kelua District, it is suggested to the Head of Paliat Village to be further improved in terms of non-physical development as well as providing a personal approach or outreach in planning for further development, and provide clarity of understanding of village fund management reports so that under development can run well. To the BPD to actively carry out supervision. To the community to always participate in participating in the implementation of development, especially training..
 
 
 

Monitoring of winter wheat stripe rust by collaborating canopy SIF with wavelet energy coefficients

Stripe rust has caused tremendous damage to wheat quality and production. Disease-specific factors revealing group structure and photosynthetic physiology contribute to high-precision monitoring for wheat stripe rust. In this study, we proposed a remote sensing model that collaborates wavelet features (WFs) and solar-induced chlorophyll fluorescence (SIF). First, sensitive features including vegetation indices (VIs), SIF parameters, WFs, and fractional-order derivative spectra (FODs) were screened based on correlation coefficient (CC) analysis and variable importance in projection (VIP). Then, through collaboration among features, six feature sets were received and imported to partial least squares regression (PLSR), back-propagation neural network (BPNN), random forest (RF), and extreme gradient boosting (XGBoost). Finally, monitoring models was evaluated through two methods: holdout cross-validation and 5-fold cross validation to ascertain the optimal feature-algorithm combination. The results demonstrated that the collaboration of canopy SIF with any feature markedly improved the monitoring accuracy due to its responsive nature to the plant's photosynthetic physiology. The model based on XGBoost with WFs-SIF as input features achieved optimal monitoring accuracy, with at least 16.6% increase in R2 and 32.4% reduction in RMSE compared to the VIs-PLSR model. Correlation analysis of evaluation indexes (R2 and RMSE) under two cross-validation methods showed determination coefficients of 0.743 and 0.837, indicating mutual validation and high reliability of the conclusions. This study suggests that the collaboration between WFs and SIF exhibits considerable feasibility in high-precision monitoring of stripe rust, providing a novel insight for future field-scale diagnosis of crop diseases.

Smart Agriculture Transformation through Deep Learning: Revolutionizing Crop Productivity and Sustainability

Deep learning, a subfield of artificial intelligence, has gained significant role in various domains, including agriculture. With the growing need to improve agricultural practices, enhance productivity, and face challenges such as crop yield prediction, disease detection, weed management, irrigation optimization, and livestock monitoring, deep learning has emerged as a valuable tool. The application of deep learning in agriculture, highlighting its potential to modernize farming practices. Overall, deep learning in agriculture holds immense promise, empowering farmers with data-driven insights, sustainability, and efficient resource utilization, increased productivity and profitability while minimizing environmental impact.