Rigorous Evaluation Research Articles

Partnerships Between Faith Communities and the Mental Health Sector: A Scoping Review.

Faith communities are increasingly providing services to address the mental health needs of their congregations and communities. However, many feel limited in their capacity to address serious illness and experience challenges to collaborating with the mental health sector. To inform the development of faith community-mental health sector partnerships, the authors conducted a scoping review to assess the characteristics and evidence base of partnership approaches to addressing mental health needs. A search of four databases identified peer-reviewed articles published between 2010 and 2023 on faith community-mental health sector partnerships in the United States. In total, 37 articles representing 32 unique partnerships were reviewed. Most partnerships (N=19) used multicomponent approaches, particularly involving training the faith community (N=18), mental health education for the broader community (N=14), and direct counseling (N=11). Many partnerships (N=14) focused on African American communities. Partnerships that included an evaluation component (N=20) showed promising findings for improving mental health symptoms, mental health literacy, stigma, and referrals, among other outcomes. Several articles reported facilitators (e.g., support from faith leaders and reciprocal relationships and equal power) and barriers (e.g., limited time and funding and differing interests and attitudes) to developing partnerships. The findings highlight how faith communities can be a critical partner in providing services across the continuum of mental health care and reveal the need for more rigorous evaluations of the effectiveness, feasibility, and sustainability of these partnerships. The results also identify strategies that may facilitate the development and strengthening of future faith community-mental health partnerships.

The Ambulatory Diabetes Outreach Program (ADOP): Rigorous Evaluation of a Pharmacist and Nurse-Led Care Model.

Although several systematic reviews found that ambulatory diabetes mellitus (DM) interventions involving pharmacists generally yielded better outcomes than the ones that did not, existing studies have limitations in rigor and study design. To examine the intention-to-treat effects of the Ambulatory Diabetes Outreach Program (ADOP) on participants' A1c values and healthcare utilization over a 52-month follow-up period. Difference-in-differences with staggered adoption. Specifically, we employed the Callaway and Sant'Anna's "group-time average treatment effect" estimator using not-yet treated as controls adjusting for patient's age, BMI, sex, race, comorbidity, payor, and socio-economic status. All patients with at least one ADOP treatment encounter from July 2017 to October 2021, regardless of program completion or length of exposure to the program. ADOP, a collaborative population health program led by pharmacists and nurse specialists to provide individualized type 2 DM management and education within a large and diverse health system. Patients' A1c values and healthcare utilization, including inpatient admission, inpatient days, and numbers of visits to the emergency department, urgent care, and primary care in recent 6months. ADOP participation was associated with an overall average reduction of 1.04 percentage points (95%CI - 1.12, - 0.95) in A1c level. Similar A1c reductions were also observed in the subgroups by sex and race/ethnicity. An average of 2months were required to reach the overall average effect, which persisted over 4years. Compared to the respective utilization levels pre-intervention, participants also had average reductions in inpatient admissions by 32.4%, inpatient days by 81.6%, visits to the emergency department by 21.6%, and primary care by 17.9%. The results suggest that a collaborative model of pharmacist and nurse-led type 2 DM intervention was effective in improving A1c outcomes and reducing healthcare utilization in the long term.

A-323 Advancing Point-of-Care Testing: Independent Evaluation of the Truvian Platform

Abstract Background To address the deficiencies of long turnaround time, increased cost and delayed medical intervention in the current paradigm of routine blood testing, Truvian has developed an automated blood testing platform that provides timely and lab-accurate results at the point of care. With 300 µL from a single Lithium Heparin blood sample, the Truvian solution generates 34 test results, including CMP, Lipid, HbA1c, TSH and CBC in under 30 minutes (TruWellness PanelTM).The present study focused on rigorous evaluation of the reliability and accuracy of Truvian's platform in a real-world clinical setting by untrained operators. Platform precision was measured over time on multiple Truvian instruments under standardized protocol. Accuracy and reliability were assessed independently at the UC San Diego Anti-Viral Research Center (AVRC) by testing samples from donors with chronic medical conditions and comparing against FDA cleared devices, Roche Cobas and Sysmex-XN. Methods Precision was assessed using the control workflow where three commercially available controls, were loaded as sample inputs into the Truvian consumables to perform the TruWellness Panel(TM). Three levels of controls were run in quadruplicate daily on 4 instruments (N > 100 per instrument). Within and between run, day, instrument, and consumable lot variance components were assessed.Accuracy was assessed as part of an IRB-approved method comparison study at AVRC by untrained operators. A total of 50 unique donors representing various disease states were consented and enrolled. Three 4-mL blood samples were collected from each donor: one Lithium Heparin (LiHep) sample for the Truvian System's TruWellness PanelTM, another LiHep gel tube for Roche Cobas clinical chemistry and TSH assays, and an EDTA sample for the Sysmex complete blood count with 3-part differential and HbA1c. All samples were tested on the Truvian System within an hour of collection or processed for central lab testing. Concordance to comparator devices was assessed via relative difference plots, Passing-Bablok and/or Deming regression analyses. Results Our results indicated that the performance of Truvian's instrument met pre-specified reliability, precision and accuracy criteria (per preliminary analysis of the first 50 patients). Reproducibility results showed 100% (69/69) of all tests met specifications. Interim analysis of the first 50 patients in the method comparison study indicates a robust concordance with predicate devices with 20 out of 23 tests showing a correlation coefficient of ≥ 0.9. During donor testing, the instrument demonstrated 96% (48/50) run reliability with only a single operator-induced error where the blood tube was not loaded correctly, highlighting the robustness and ease of use of the platform. Conclusions The present evaluation underscores Truvian's instrument as an easy-to-operate platform that can be run by untrained operators, maintain reliability in real-world clinical settings, and deliver accurate and precise results. Enrollment and sample testing are ongoing, with plans to expand the sample size to 200 donors and study completion planned for May 2024. Broadening the study to encompass a wider range of analyte measurements among chronic disease patients is expected to enhance performance data. This independent evaluation provided confidence in Truvian Platform’s performance and suitability for practical healthcare applications.

Algorithm for Key Transparency with Transparent Logs

Background Cryptography plays a crucial role in securing digital communications and data storage. This study evaluates the Transparent Key Management Algorithm utilizing Merkle trees, focusing on its performance and security effectiveness in cryptographic key handling. Methods The research employs simulated experiments to systematically measure and analyze key operational metrics such as insertion and verification times. Synthetic datasets are used to mimic diverse operational conditions, ensuring rigorous evaluation under varying workloads and security threats. Implementation is carried out using R programming, integrating cryptographic functions and Merkle tree structures for integrity verification. Results Performance analysis reveals efficient insertion and verification times under normal conditions, essential for operational workflows. Security evaluations demonstrate the algorithm's robustness against tampering, with approximately 95% of keys verified successfully and effective detection of unauthorized modifications. Simulated attack scenarios underscore its resilience in mitigating security threats. Conclusions The Transparent Key Management Algorithm, enhanced by Merkle trees and cryptographic hashing techniques, proves effective in ensuring data integrity, security, and operational efficiency. Recommendations include continuous monitoring and adaptive algorithms to bolster resilience against evolving cybersecurity challenges, promoting trust and reliability in cryptographic operations.

Energy Harvesting Deadline Monotonic Approach for Real-time Energy Autonomous Systems

This paper presents an innovative scheduling algorithm designed specifically for real-time energy harvesting systems, with a primary focus on minimizing energy consumption and extending the battery's lifespan. The algorithm employs a fixed priority assignment which is the deadline monotonic policy, we have chosen it for its optimality and superior performance compared to other fixed priority scheduling methods. To achieve a balance between energy efficiency and system performance, we incorporated a DVFS (Dynamic Voltage and Frequency Scaling) technique into the algorithm. This adaptive approach enables precise control over the processor's operating frequency, effectively managing energy consumption while ensuring satisfactory system functionality. The core objective of our scheduling algorithm centers on optimizing energy utilization in real-time energy harvesting systems, specifically tailored to extend the battery's operational life. Rigorous evaluations, including comprehensive comparisons against established fixed priority scheduling algorithms, validate the algorithm's efficacy in significantly reducing energy consumption while preserving the system's overall functionality. By combining the deadline monotonic policy and DVFS technique, our proposed algorithm emerges as a promising solution for energy-autonomous systems, contributing to the advancement of sustainable energy practices in real-time applications. As energy harvesting technologies continue to progress, our algorithm holds valuable potential to provide critical insights for enhancing the efficiency and reliability of future energy harvesting systems.

Medication Extraction and Drug Interaction Chatbot: Generative Pretrained Transformer-Powered Chatbot for Drug-Drug Interaction

ObjectiveTo assist individuals, particularly cancer patients or those with complex comorbidities, in quickly identifying potentially contraindicated medications when taking multiple drugs simultaneously. Patients and MethodsIn this study, we introduce the Medication Extraction and Drug Interaction Chatbot (MEDIC), an artificial intelligence system that integrates optical character recognition and Chat generative pretrained transformer through the Langchain framework. Medication Extraction and Drug Interaction Chatbot starts by receiving 2 drug bag images from the patient. It uses optical character recognition and text similarity techniques to extract drug names from the images. The extracted drug names are then processed through Chat generative pretrained transformer and Langchain to provide the user with information about drug contraindications. The MEDIC responds to the user with clear and concise sentences to ensure the information is easily understandable. This research was conducted from July 2022 to April 2024. ResultsThis streamlined process enhances the accuracy of drug-drug interaction detection, providing a crucial tool for health care professionals and patients to improve medication safety. The proposed system was validated through rigorous evaluation using real-world data, reporting high accuracy in drug-drug interaction identification and highlighting its potential to benefit medication management practices considerably. ConclusionBy implementing MEDIC, contraindicated medications can be identified using only medication packaging, and users can be alerted to potential drug adverse effects, thereby contributing to advancements in patient care in clinical settings.

Microplastics originated from Plasmix-based materials caused biochemical and behavioral adverse effects on Daphnia magna

The implementation of advanced recycling techniques represents a key strategy for mitigating the mismanagement and the environmental impact of plastic waste. A limited array of plastic polymers can be efficiently recycled, while a notable portion of plastic waste remains unrecyclable. In Italy, this residual, heterogeneous fraction is referred to as Plasmix. Because of its complexity and non-homogeneous composition, Plasmix is primarily directed towards low-value applications. However, recent developments in laboratory-scale mechanical recycling have enabled the creation of new plastic materials from Plasmix. Prior to their application, these materials must undergo rigorous eco-safety evaluation. The present study aims to assess the potential toxicity of microplastics (MPs) from Plasmix-based materials on the freshwater crustacean Daphnia magna. Specifically, this study investigated sub-individual and individual effects induced by a 21-day exposure to different concentrations of MPs generated from the grinding of naïve and Additivated Plasmix-based materials (hereafter referred to as Px-MPs and APx-MPs, respectively). Sub-individual endpoints focused on changes in oxidative status, including the modulation of antioxidant and detoxifying enzyme activities, as well as oxidative damage, such as lipid peroxidation. Individual level endpoints included alterations in survival and reproduction. Microscopy analyses confirmed the ingestion of both Px-MPs and APx-MPs by D. magna individuals. An oxidative stress condition raised in organisms exposed to Px-MPs, whereas no effect was observed in individuals exposed to APx-MPs. Although survival was not affected, a significant impairment in reproductive output was detected at the end of exposure to all the concentrations of both MP types. These findings suggest that even low concentrations of Px-MPs and APx-MPs could negatively affect the health status of D. magna, underscoring the need for further research to complete the risk assessment of Plasmix-based materials prior to their use in consumer products.

IOT NETWORK INTRUSION DETECTION USING MACHINE LEARNING ON UNSW-NB15 DATASET

This research presents a comprehensive investigation into the application of machine learning techniques for addressing the pervasive security challenges within Internet of Things (IoT) networks. With the exponential growth of interconnected devices, ensuring the integrity and confidentiality of data transmissions has become increasingly critical. In this study, we deploy and evaluate seven distinct machine learning methods tailored to the IoT network intrusion detection problem. Leveraging the rich and diverse UNSW-NB15 dataset, encompassing real-world network traffic scenarios, our analysis encompasses a thorough examination of both traditional and state-of-the-art algorithms. Through rigorous experimentation and performance evaluation, we assess the efficacy of these methods in accurately detecting and classifying various forms of network intrusions. Our findings provide valuable insights into the strengths and limitations of different machine learning approaches for enhancing the security posture of IoT environments, thereby facilitating informed decision-making for network administrators and cybersecurity practitioners.

Toward the Rigorous Evaluation of Early Warning Scores

Toward the Rigorous Evaluation of Early Warning Scores

A novel approach for detecting Pomegranate leaf pathologies using deep learning

Pomegranate cultivation is essential for ensuring global food security, yet it faces significant threats from diseases. This has resulted in significant losses of yield. Integrated disease management strategies are therefore crucial for addressing this problem and include cultural practices, disease resistant varieties and timely application of fungicides. Artificial intelligence (AI), primarily deep learning, has recently made great strides that can help change the landscape on how agricultural diseases are diagnosed. In this study, therefore, we will develop a highly accurate and efficient system for early detection of diseases in pomegranate crops using deep learning algorithms. Using convolutional neural networks (CNNs) along with transfer learning coupled with data augmentation techniques such as rotation, flipping and scaling automate the identification and classification of disorders related to leaves growing on pomegranate plants. Rigorous experimentation and evaluation using performance metrics like accuracy, precision, recall, and F1 score demonstrate the efficacy of the CNN models. The underlying theory behind this is that AI technologies would be able to successfully detect all BLB diseases associated with pomegranates at an accuracy rate of 96.33 percent using Inception v3 CNN model. These results point out that AI technology changes everything about dealing with crop diseases in agriculture while also emphasizing its importance towards global food security efforts. This study not only presents a novel approach to disease detection but also provides practical solutions for farmers to mitigate crop losses and improve yield quality, promoting more sustainable and resilient farming.

Implementing an Educational Design Based on Hypermedia to Enhance Creative Imagination Among Fifth-Grade Literary Students

The research delves into the intricate design and effectiveness of hypermedia-based educational systems, emphasizing their pivotal role in enhancing learning processes through spatially induced learning formats and creative tools. Hypermedia, a sophisticated form of computer-based media technology, facilitates non-linear, feature-rich media interfaces that significantly augment educational content delivery. This technology transcends traditional instructional tools by embedding navigational methods tailored to diverse learning tasks and environments, thereby fostering deeper understanding and engagement among learners. In this context, the ADDIE model a structured approach comprising Analysis, Design, Development, Implementation, and Evaluation—is crucial for the systematic development of educational programs. Each phase of ADDIE builds upon the insights gained from the previous, starting with a comprehensive analysis of training needs, followed by the design and development of instructional materials that incorporate effective strategies and media. The implementation phase involves deploying these materials through learning management systems, ensuring accessibility and alignment with educational goals. Finally, the evaluation phase seeks feedback to refine and enhance the learning modules, demonstrating ADDIE’s iterative nature and its focus on continuous improvement. Moreover, the study engages with a broader theoretical and methodological discourse, integrating perspectives from instructional design and creative imagination within the framework of hypermedia technology. This integration supports the construction of a learning environment that not only addresses theoretical knowledge but also stimulates creativity and innovation among students. The effectiveness of these instructional strategies is evidenced through rigorous assessments and evaluations, highlighting the significant impact of hypermedia on student creativity, problem-solving skills, and overall academic performance. This comprehensive approach not only validates the use of hypermedia in educational settings but also outlines potential improvements and future directions for integrating technology in learning processes, thereby contributing to the evolving landscape of educational technology and pedagogy.

Improved Data Privacy with Differential Privacy in Federated Learning

Multiple users may train machine learning models cooperatively using Federated Learning (FL). There is a risk of malicious acquisition of participants' personal data due to the fact that traditional machine learning needs users to provide data for training. Through the use of federated learning, which involves moving the training process from a central server to terminal devices, users' data may be protected. Each participant keeps their dataset local and only exchanges model updates. This research proposed an innovative proposal for the medical industry's differentiated privacy approach for overcoming these problems. When several healthcare organizations work together to develop models that use different and extensive information, clinical applications may be greatly enhanced. Thus, the Local and Centre Differential Privacy (LCDP) on clinical datasets is a feature of our proposed approach. Reason being that the training data is the primary emphasis of the local model, while the machine learning model is the primary focus of the central model. We discover that the local model and the central model are linked in a unique way, changes in the original data lead to changes in the gradient, which in turn lead to changes in the model parameters. Based on this finding, our technique is better than prior central methods since it protects the data, gradient, and model all at once by bridging the gap between the two. Our system provides better privacy protections and even higher performance than some of the best previous central methods, which is an excellent outcome of rigorous evaluation.

The Future of Regenerative Medicine: Stem Cell Research in Pakistan

The landscape of medicine is being revolutionized by the recent breakthroughs in stem cell research that offer new possibilities for treating diseases that were once deemed degenerative, incurable, or irreversible. In low and middle-income countries like Pakistan stem cell therapy holds promise for cost-effective solutions to non-communicable diseases. However, these advancements are hindered by ongoing debates, legal challenges, and public controversies. Stem cells can differentiate into various cell types and self-renew themselves, making them appealing for regenerative medicine. Unlike traditional therapies, regenerative medicine focuses on the restoration of optimal functioning of the damaged tissues and organs. Stem cells can be categorized as embryonic and adult stem cells. Adult stem cells such as mesenchymal stem cells can differentiate into various cell types. These can be obtained from bone marrow, adipose tissue, umbilical cord tissue, and amniotic fluid. Research shows that stem cells have the potential to treat cancers and advance regenerative medicine. Genetically modified stem cells can act as delivering systems for the treatment of genetic disorders and the development of therapeutic agents directly targeted to organs. Successful differentiation of stem cells into neurons, cardiomyocytes, insulin-producing cell clusters, hepatocytes, and hematopoietic precursors. These achievements are powerful tools that can help in combating human diseases. In Pakistan, while the potential of stem cell therapy has generated significant excitement, the progress is hindered by regulatory and infrastructure challenges. Addressing these concerns and fostering a supportive research environment is essential for advancing stem cell and regenerative medicine in the country. According to the WHO International Clinical Trials Registry, there are over 3000 trials on adult stem cells in progress [1]. These trials are essential in advancing the knowledge of stem cell therapies and for the establishment of realistic expectations for their outcomes. The transplantation of stem cells into non-native environments can pose risks, including tumor formation and other complications. These risks underscore the importance of rigorous evaluation and monitoring to ensure that these promising therapies are both safe and effective before they become widely available. In conclusion, stem cell research has great potential to revolutionize healthcare in Pakistan, but ethical, regulatory, and infrastructure challenges must be overcome before its benefits can be fully realized in regenerative medicine. An environment that is supportive and safe is imperative for clinical trials and research to ensure safe and effective therapies in the future.

Time Series Forecasting of Emission Trends Using Recurrent Neural Networks

This paper explores the application of Recurrent Neural Networks (RNNs) for forecasting emission trends, a critical aspect of addressing climate change and formulating effective environmental policies. Traditional forecasting methods, such as Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing, often fail to capture the complex nonlinear relationships and temporal dependencies inherent in emission data. In contrast, RNNs, particularly Long Short-Term Memory (LSTM) networks, are designed to recognize patterns in sequential data, making them well-suited for time series forecasting tasks. This study employs a comprehensive methodology that includes data collection from reputable sources, feature selection, RNN model design, and rigorous evaluation using various performance metrics. The results indicate that RNNs significantly outperform traditional forecasting techniques in terms of accuracy, providing valuable insights into future emission trajectories. The findings underscore the potential of RNNs as powerful tools for policymakers and researchers, facilitating more informed decision-making in the fight against climate change.

Efficient DC motor speed control using a novel multi-stage FOPD(1 + PI) controller optimized by the Pelican optimization algorithm.

This paper introduces a novel multi-stage FOPD(1 + PI) controller for DC motor speed control, optimized using the Pelican Optimization Algorithm (POA). Traditional PID controllers often fall short in handling the complex dynamics of DC motors, leading to suboptimal performance. Our proposed controller integrates fractional-order proportional-derivative (FOPD) and proportional-integral (PI) control actions, optimized via POA to achieve superior control performance. The effectiveness of the proposed controller is validated through rigorous simulations and experimental evaluations. Comparative analysis is conducted against conventional PID and fractional-order PID (FOPID) controllers, fine-tuned using metaheuristic algorithms such as atom search optimization (ASO), stochastic fractal search (SFS), grey wolf optimization (GWO), and sine-cosine algorithm (SCA). Quantitative results demonstrate that the FOPD(1 + PI) controller optimized by POA significantly enhances the dynamic response and stability of the DC motor. Key performance metrics show a reduction in rise time by 28%, settling time by 35%, and overshoot by 22%, while the steady-state error is minimized to 0.3%. The comparative analysis highlights the superior performance, faster response time, high accuracy, and robustness of the proposed controller in various operating conditions, consistently outperforming the PID and FOPID controllers optimized by other metaheuristic algorithms. In conclusion, the POA-optimized multi-stage FOPD(1 + PI) controller presents a significant advancement in DC motor speed control, offering a robust and efficient solution with substantial improvements in performance metrics. This innovative approach has the potential to enhance the efficiency and reliability of DC motor applications in industrial and automotive sectors.

Real-Time Co-Simulation Implementation for Voltage and Frequency Regulation in Standalone AC Microgrid with Communication Network Performance Analysis across Traffic Variations

Effective communication networks are crucial for ensuring reliable and stable operation and control in smart microgrids (MGs). This paper proposes a comprehensive analysis of the interdependence between power and communication networks in the real-time control of a standalone AC microgrid to address this vital need. Thus, the role of communication network design is emphasized in facilitating an effective centralized secondary control to regulate the voltage and frequency of an MG. Consequently, voltage and frequency deviations from the droop-based primary control should be eliminated. This study employs a real-time co-simulation testbed setup that integrates OPAL-RT and network simulator (ns-3), supporting a rigorous evaluation of the interplay between the communication networks and control within the MG. Experiments have been conducted to demonstrate the effectiveness of the designed communication infrastructure in seamlessly enabling real-time data exchange among the primary and secondary control layers. Testing scenarios have been implemented, encompassing low-traffic patterns with minimal load variations and high traffic characterized by more frequent and severe load changes. The experimental results highlight the significant impact of traffic variations on communication network performance. Despite the increase in traffic, the effectiveness and reliability of the designed communication network have been validated, underscoring the vital role of communication in ensuring the resilient and stable operation of cyber–physical standalone AC microgrids.

InfoGCN++: Learning Representation by Predicting the Future for Online Skeleton-based Action Recognition.

Skeleton-based action recognition has made significant advancements recently, with models like InfoGCN showcasing remarkable accuracy. However, these models exhibit a key limitation: they necessitate complete action observation prior to classification, which constrains their applicability in realtime situations such as surveillance and robotic systems. To overcome this barrier, we introduce InfoGCN++, an innovative extension of InfoGCN, explicitly developed for online skeletonbased action recognition. InfoGCN++ augments the abilities of the original InfoGCN model by allowing real-time categorization of action types, independent of the observation sequence's length. It transcends conventional approaches by learning from current and anticipated future movements, thereby creating a more thorough representation of the entire sequence. Our approach to prediction is managed as an extrapolation issue, grounded on observed actions. To enable this, InfoGCN++ incorporates Neural Ordinary Differential Equations, a concept that lets it effectively model the continuous evolution of hidden states. Following rigorous evaluations on three skeleton-based action recognition benchmarks, InfoGCN++ demonstrates exceptional performance in online action recognition. It consistently equals or exceeds existing techniques, highlighting its significant potential to reshape the landscape of real-time action recognition applications. Consequently, this work represents a major leap forward from InfoGCN, pushing the limits of what's possible in online, skeleton-based action recognition.

Leveraging Machine Learning Methods for Predicting Employee Turnover Within the Framework of Human Resources Analytics

Employee turnover is a critical challenge for organizations, leading to significant costs and disruptions. This study aims to leverage Machine Learning (ML) techniques within the framework of Human Resources Analytics (HRA) to predict employee turnover effectively. The research evaluates and compares the performance of six widely used models: Decision Trees, Support Vector Machines (SVM), Logistic Regression, Random Forest, XGBoost, and Artificial Neural Networks. These models were implemented using the R programming language on an open-source dataset from IBM. The methodology involved data preprocessing, splitting into training, validation and testing sets, model training, and performance evaluation using metrics such as accuracy, sensitivity, specificity, precision, F1-score, and ROC-AUC. The results indicate that the Logistic Regression model outperformed the other models, achieving high accuracy and a good F1-score. The study concludes by emphasizing the importance of HRA and ML techniques in predicting and managing employee turnover, while discussing limitations such as class imbalance and the need for more rigorous performance evaluation. Future research directions include exploring alternative models, feature selection techniques, and addressing class imbalance.

Cadastral-to-Agricultural: A Study on the Feasibility of Using Cadastral Parcels for Agricultural Land Parcel Delineation

Agricultural land parcels (ALPs) are essential for effective agricultural management, influencing activities ranging from crop yield estimation to policy development. However, traditional methods of ALP delineation are often labor-intensive and require frequent updates due to the dynamic nature of agricultural practices. Additionally, the significant variations across different regions and the seasonality of agriculture pose challenges to the automatic generation of accurate and timely ALP labels for extensive areas. This study introduces the cadastral-to-agricultural (Cad2Ag) framework, a novel approach that utilizes cadastral data as training labels to train deep learning models for the delineation of ALPs. Cadastral parcels, which are relatively widely available and stable elements in land management, serve as proxies for ALP delineation. Employing an adapted U-Net model, the framework automates the segmentation process using remote sensing images and geographic information system (GIS) data. This research evaluates the effectiveness of the proposed Cad2Ag framework in two U.S. regions—Indiana and California—characterized by diverse agricultural conditions. Through rigorous evaluation across multiple scenarios, the study explores diverse scenarios to enhance the accuracy and efficiency of ALP delineation. Notably, the framework demonstrates effective ALP delineation across different geographic contexts through transfer learning when supplemented with a small set of clean labels, achieving an F1-score of 0.80 and an Intersection over Union (IoU) of 0.67 using only 200 clean label samples. The Cad2Ag framework’s ability to leverage automatically generated, extensive, free training labels presents a promising solution for efficient ALP delineation, thereby facilitating effective management of agricultural land.

Exploring the Therapeutic Potential of Sphenocentrum jollyanum Root Extract in Malaria Treatment: Efficacy, Safety, and Future Directions

Malaria remains a critical global health challenge, necessitating the exploration of novel therapeutic agents. This review examines the therapeutic potential of the ethanol root-extract of Sphenocentrum jollyanum, a plant traditionally used in West Africa for malaria treatment. Utilizing the Plasmodium berghei rodent model, which mirrors human malaria, we assess the extract’s efficacy in restoring haematological markers, safety profile, and potential as an adjunct or alternative therapy. Sphenocentrum jollyanum, known for its rich phytochemical composition, including alkaloids, flavonoids, saponins, tannins, and terpenoids, has demonstrated significant restorative effects in preliminary studies. The extract improved hemoglobin levels, red blood cell counts, and platelet counts in infected mice, indicating potential benefits in mitigating malaria-induced anemia and other haematological abnormalities. Its mechanisms of action include antioxidant, anti-inflammatory, and immunomodulatory effects. Despite its promising results, rigorous safety evaluations are necessary, including acute and chronic toxicity studies, to ensure its safe use. Future research should focus on clinical trials to validate efficacy, optimize dosage, and explore combination therapies with standard antimalarial drugs. The review emphasizes the need for standardized preparation methods, quality control, and integration into public health strategies to enhance malaria management, particularly in resource-limited regions. Keywords: Sphenocentrum jollyanum Root Extract, Malaria, Treatment, Efficacy, Safety.