Quick Identification Research Articles

Drought Dynamics in Sub-Saharan Africa: Impacts and Adaptation Strategies

The escalation in both frequency and severity of drought events has significantly amplified the vulnerability of numerous countries, particularly in developing ones, imposing substantial economic, environmental, and social pressures. This article presents a systematic review of drought occurrences in Sub-Saharan Africa (SSA), examining historical trends, current impacts, and projected future implications. Through this comprehensive assessment, a clear trend of intensifying drought phenomena emerges across SSA, leading to crop failures, drying of water sources, loss of pasture, food shortages, and an increase in food prices. This review also highlights the concerning potential for worsening conditions in certain regions, resulting in consequences such as migration, food insecurity, malnutrition, family disintegration, crop losses, and increased disease prevalence, notably HIV/AIDS. This study further reveals that current adaptation measures by governments and NGOs should be improved to effectively adapt to the diverse impacts of drought, and it contributes to a deeper understanding of drought dynamics in Sub-Saharan Africa and assesses its critical impacts on food security and social well-being. It also evaluates adaptation measures across different countries, highlighting their strengths and weaknesses and enabling quick identification of areas for improvement. Additionally, it informs resilience-building efforts in vulnerable communities.

HoloLume: Point-of-Application Holographic Imaging Solution

Abstract Digital holographic imaging has emerged as a label-free, non-invasive, and powerful tool, offering a transformative method for the real-time investigation of morphology, density, and other physical and mechanical features of biological and non-biological samples. The rapid development of these portable, economical imaging systems for clinical/non-clinical setups, especially in remote areas, is of great interest. These systems are early point-of-care applications in diagnosis, research, education, and training. This article reports a general-purpose, fully automated portable imaging tool, "HoloLume," that offers high-resolution 3D imaging that facilitates the comprehensive understanding and analysis of different biological/non-biological samples. The proposed microscopic system HoloLume realizes the interferogram generation of samples using common-path optics configuration with Fresnel biprism, and its motorized stage precisely scans the whole sample with a step of 3.87 μm for quick identification of the region of interest, which makes sure a time-efficient and robust analysis. The prototype, assembled from off-the-shelf components, costs approximately USD 1700 and weighs around 3 kg. To validate and verify the performance of the HoloLume microscope, we have conducted experiments on both non-biological and biological samples. Thus, its non-invasive nature, high temporal stability, and economical lightweight prototyping make it an ideal choice for hands-on learning and quantitative analysis, reducing the need for time-consuming sample preparation and conventionally expensive microscopic systems. Furthermore, the adaptability of the proposed imaging tool to remote environments ensures wider accessibility and its easy integration with other imaging modalities enables the user to gain insight into advanced research and diagnostics.

The Spatiotemporal Evolution of the Mudflat Wetland in the Yellow Sea Using Landsat Time Series

Mudflat wetland, one of the 27 surface elements identified by the International Geographic Data Committee, has undergone substantial transformations with the rapid growth of the social economy and marine hazards, resulting in significant changes in its area and distribution. Quick identification of mudflat wetland evolution is vital to improve the mudflat ecological service value. We employed object-oriented and decision tree classification methods to map the mudflat wetland in the Yellow Sea using the Landsat time series from 1983 to 2020. The Improved Spectral Water Index (IWI) was established by combining the characteristics of many ratio indices and using ratio operation and quadratic power operation. The coefficient of variation (CV) of the IWI was calculated, and the range of the intertidal zone in 1983, 1990, 2000, 2010, and 2020 was obtained by using a threshold method. The results indicate that the mudflat wetland area decreased continuously from 1983 to 2020, with a reduction of 337.38 km2/10a. Among the total area, the natural wetland experienced a decline of 446.9 km2/10a, with the most drastic changes occurring between 2000 and 2010. In contrast, the area of the human-made wetland increased by 109.56 km2/10a. Over the 38 years, the tidal flat has undergone the most drastic reduction, with an average of 157.45 km2/10a. From 1983 to 2020, the intertidal zone area decreased, with a reduction of 429.02 km2/10a. Human activities were the key factors causing mudflat wetland loss. Based on these findings, we propose several policy suggestions. This study provides a scientific basis for understanding the synergetic evolution mechanism of coastal resources utilization and mudflat wetland protection under global change.

Design and fabrication of PCF‐based terahertz sensor for breast cancer cell detection

AbstractBreast cancer is a type of cancer that is common in women worldwide, which emphasises its significance in identification with preventative treatment methods. The invented Photonic Crystal Fibre (PCF) exhibits outstanding performance in detecting Breast Cancer. The suggested model of the authors includes Hybrid layout within clad surface alongside Square Core. Introduced PCF detector exhibits max Relative Sensitivity (RS) of 96.82% as well 96.74% for breast cancer cell MCF‐7 as well MDA‐MB‐231 correspondingly. The authors also investigated the Confinement Loss of 1.642 × 10−10 dB/m, 2.461 × 10−10 dB/m with Effective Material Loss of 0.0473, 0.0565 cm−1 for the mentioned cells. Increased outcomes, customised therapy, plus quick action are made possible by swift identification in breast carcinoma. Timely malignancy detection reduces requirements to severe therapy by enabling simpler medicines. Additionally, making continuous illness detection easier, improving patient treatment. Furthermore, reliable evaluation contributes for investigating advancements that improve worldwide recognition as well as therapy alternatives. The introduced PCF Perhaps crucial in quick identification of these deadly cells as it has an extraordinary sensing ability. In conclusion, it has numerous possibilities in the healthcare sector.

A Review on Traceability System for Fruit and Vegetables Agricultural Products Using Blockchain

This paper presents a blockchain-based traceability system designed to enhance the transparency and efficiency of the agricultural supply chain for fruits and vegetables. By leveraging blockchain technology, the system enables real-time tracking of products from farm to consumer, ensuring data integrity and reducing the risk of food fraud. Each transaction is recorded on a decentralized ledger, allowing stakeholders—farmers, distributors, retailers, and consumers—to access accurate information regarding product origins, handling processes, and quality metrics. This traceability system not only fosters consumer trust but also assists in compliance with regulatory standards and enhances food safety by enabling quick identification of contamination sources. The implementation of this solution can significantly improve supply chain management, reduce losses, and promote sustainable agricultural practices, ultimately benefiting both producers and consumers

First Case of Chryseobacterium gleum Post-COVID-19 in a Child with Recurrent Fever

Background and Clinical Significance:Chryseobacterium gleum is a Gram-negative opportunistic and emerging pathogen able to induce systemic manifestations (e.g., peritonitis, pneumonia, urinary tract infections, meningitis) in immunocompromised patients. No data on children have been published. Case Presentation: A 2-year-old child presented in the pediatric ambulatory room with recurrent fever, submandibular lymphadenopathy, and skin rash. Laboratory findings revealed the presence of microcytic anemia with an increase in c-reactive protein. Chest X-ray reported mild accentuation of the bronchial structure, especially on the right side and middle–lower zone. In the peripheral blood smear, anisopoikilocytosis and elliptical red cells were evident. Clinical evaluation revealed the presence of conjunctivitis and polymorphic erythema, hyperemic pharynx and tonsils, SPO2 99%, auscultation of the chest, harsh vesicular murmur all over the area, and some wheezing. Microbiological analysis of sputum and throat swabs revealed the presence of numerous colonies of Chryseobacterium gleum confirmed using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS score > 2.2). Conclusions: This is the first case of Chryseobacterium gleum post-COVID in a child. We suggest that a quick identification and an appropriate treatment represent the critical factors able to prevent the adverse outcomes related to C. gleum infection.

Exploring the Applications of Artificial Intelligence in Dental Image Detection: A Systematic Review

Background: Dental care has been transformed by neural networks, introducing advanced methods for improving patient outcomes. By leveraging technological innovation, dental informatics aims to enhance treatment and diagnostic processes. Early diagnosis of dental problems is crucial, as it can substantially reduce dental disease incidence by ensuring timely and appropriate treatment. The use of artificial intelligence (AI) within dental informatics is a pivotal tool that has applications across all dental specialties. This systematic literature review aims to comprehensively summarize existing research on AI implementation in dentistry. It explores various techniques used for detecting oral features such as teeth, fillings, caries, prostheses, crowns, implants, and endodontic treatments. AI plays a vital role in the diagnosis of dental diseases by enabling precise and quick identification of issues that may be difficult to detect through traditional methods. Its ability to analyze large volumes of data enhances diagnostic accuracy and efficiency, leading to better patient outcomes. Methods: An extensive search was conducted across a number of databases, including Science Direct, PubMed (MEDLINE), arXiv.org, MDPI, Nature, Web of Science, Google Scholar, Scopus, and Wiley Online Library. Results: The studies included in this review employed a wide range of neural networks, showcasing their versatility in detecting the dental categories mentioned above. Additionally, the use of diverse datasets underscores the adaptability of these AI models to different clinical scenarios. This study highlights the compatibility, robustness, and heterogeneity among the reviewed studies. This indicates that AI technologies can be effectively integrated into current dental practices. The review also discusses potential challenges and future directions for AI in dentistry. It emphasizes the need for further research to optimize these technologies for broader clinical applications. Conclusions: By providing a detailed overview of AI’s role in dentistry, this review aims to inform practitioners and researchers about the current capabilities and future potential of AI-driven dental care, ultimately contributing to improved patient outcomes and more efficient dental practices.

An Automated Computational Fluid Dynamics Workflow for Simulating the Internal Flow of Race Car Radiators

In this article, we present a software tool developed in Python, named T-WorkFlow. It has been devised to meet some of the design needs of Tatuus Racing S.p.a., a leading company in the design and production of racing cars for the FIA Formula 3 Regional and Formula 4 categories. The software leverages the open-source tools OpenFOAM and FreeCAD to fully automate the fluid dynamics simulation process within car radiators. The goal of T-WorkFlow is to provide designers with precise and easily interpretable results that facilitate the identification of the geometry, ensuring optimal flow distribution in the radiator channels. T-WorkFlow requires the radiator’s geometry files in .stp and .stl formats, along with additional user inputs provided through a graphical interface. For mesh generation, the software leverages the OpenFOAM tools blockMesh and snappyHexMesh. To ensure uniform mesh quality across different configurations, and thus, comparable numerical results, various pre-processing operations on the specific geometry files are needed. After generating the mesh, T-WorkFlow automatically defines a control surface for each radiator channel to monitor the volumetric flow rate distribution. This is achieved by combining the OpenFOAM command topoSet with specific geometric information directly obtained from the radiator’s CAD through FreeCAD. During the simulation, the software provides various outputs that automate the main post-processing operations, enabling quick and easy identification of the configuration that ensures the desired performance.

Research work of Prof. De-Eknamkul, including the mechanistic synergy of hair growth promotion by the Avicennia marina extract and its active constituent (avicequinone C) in dermal papilla cells isolated from androgenic alopecia patients

Androgenetic alopecia (AGA) is a genetically-predetermined disorder that affects many people around the world, leading to progressive hair loss. This baldness can negatively impact people’s perceptions of themselves and scientists are searching for a cure. There are only two US Food and Drug Administration-approved drugs used to treat AGA and they both have side effects. If a natural anti-AGA product could be developed with the ability to be effective minus negative side effects, this could be a game-changer for people with AGA. Professor Wanchai De-Eknamkul, Department of Pharmacognosy and Pharmaceutical Botany, Chulalongkorn University, Thailand, an expert in plant natural product biotechnology, is working to develop a natural anti-AGA product. He is a professor at the University’s Natural Product Biotechnology Research Unit, where research is underway to: elucidate the biosynthesis of bioactive natural products by searching for enzymes and genes involved in their biosynthetic pathways; discover bioactive natural compounds from medicinal plants by either enzyme-based or cell-based screening followed by studying their mechanism of actions; and develop high-performance thin-layer chromatography (HPTLC) as a tool for quick screening and identification of bioactive compounds. De-Eknamkul have been working to shed light on the genes and enzymes involved in the biosynthesis of the diterpenoid ‘plaunotol’ in Croton stellatopilosus. In another line of investigation, they are searching for natural compounds with anti-AGA and anti-grey hair activities and a further area of focus is the development of a technique for direct detection of compounds with anti-diabetes, anti-gonorrhoea and anti-AGA activities.

Intravenous Line Labels For High-Alert Drugs Administered To Critically Ill Patients: A Simulated Experimental Assessment.

Evaluate the effect of IV line labels on nurses' identification of high-alert medications in a simulated scenario of multiple infusions for critically ill patients. Randomised crossover simulation experimental study. A study was conducted on 29 nurses working in intensive care for over 6 months. They were given two critical scenarios in a simulated environment, one with labels and the other without labels, involving multiple intravenous infusions. The nurses had to identify the medications infused into the critical patients' intravenous lines and disconnect a specific line. The data were collected and analysed to evaluate the errors made by the nurses in identifying and disconnecting the medications and the time they spent carrying out the tasks. The Wilcoxon test was used to analyse the variation in outcome before and after the intervention. Approximately one-third of the study participants incorrectly identified the intravenous lines in both scenarios. There was no significant difference in the average number of errors between the scenarios with and without labels. However, the time taken to perform the tasks in the scenario with labels was 1 min less than in the scenario without labels, suggesting a potential efficiency gain. The labels on the intravenous lines allowed for quick drug identification and disconnection. The professionals performed similarly in correctly recognising the high-alert medication intravenous lines, in the scenarios with or without labels. The label can be used as a technology to prevent misidentification of high-alert medications administered to critically ill patients through intravenous lines, thereby enhancing medication safety in healthcare institutions. No Public Contribution.

Rapid and high-accuracy concentration prediction of gas mixtures based on PMH-TCN

In industrial environments, the flammable gas ethylene (C2H4) and toxic gas carbon monoxide (CO) often coexist. To ensure worker safety, it is essential to predict these gas concentrations quickly and accurately. Many studies lack prediction accuracy or speed due to insufficient time feature extraction and loss of critical features. In this work, a novel approach called the Parametric Rectified Linear Unit (PReLU)-based Multi-Head Attention Temporal Convolutional Network (PMH-TCN) model was devised to efficiently and precisely predict gas concentrations in mixtures. The model is capable of accurately predicting the gas concentration within 5 s after the introduction of the target gas. In the 5-fold cross-validation, the values of Adjusted R-Square (R2), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of the PMH-TCN model reached 0.9850, 0.0448, and 0.0651 respectively. It was shown that the PMH-TCN model offered a very efficient method for the quick identification of electronic noses.

Modular soft pneumatic actuator mimics elephant trunk locomotion

Robots support and facilitate tasks in all life fields. Soft robots specifically have the advantages of inherent compliance, safe interaction and flexible deformability. Soft pneumatic network (Pneu-Net) is a soft pneumatic actuator (SPA) composed of network of chambers that is actuated by pneumatic power. Soft Pneu-Net fits the human interface applications perfectly. In this paper, a bio-inspired modular based design for Pneu-Net actuator is developed. The actuator mimics the elephant trunk curling to be employed for rehabilitation of human hand fingers. The actuator is an integrated four Pneu-Net modules actuator which is attached to hand’s finger. The main introduced advantages in the new developed actuator are: providing four degrees of freedom (DoF) essential for finger’s motion by single compound actuator and developing a methodology for a modular soft Pneu-Net actuator that is efficiently reproducible. The actuator’s design is developed using computer aided design (CAD) software SOLIDWORKS. The design is simulated using finite element modeling (FEM) software ABAQUS. Fabrication process uses 3D printed molds. Soft material is molded in the 3D printed molds, forming actuator’s modules. Actuator’s modules are integrated by adhesion using the soft material. A proposed non-standard hyper-elastic material biaxial tension test is introduced as a quick material properties identification method that can produce a test table used for material identification in the FEM. Enhanced version for the actuator uses reinforcement fibers. Results show advances for the reinforced actuator, as it limits the unwanted actuator’s strain and deformation. The reinforced actuator shows improved energy efficiency reaches to 46%.

EffSVMNet: An efficient hybrid neural network for improved skin disease classification

The Human Body’s primary defense layer is the skin which protects important organs from various external assaults. This organ protects our internal systems, safeguarding them from possible injury caused by viruses, fungus, and other factors. Unfortunately, the skin is not impenetrable, and infections or damage can occur, which leads to serious problems of health. Even a little skin lesion has the power to become a huge issue. As a result, in our study, our target is to produce an effective system for the quick and early identification of skin illnesses using well-known Convolutional Neural Networks (CNNs). The idea is to use this specialized neural network architecture to improve and speed up the detection and classification process to reduce time-lagging for treatment options. The proposed model i.e., EffSVMNet is a hybrid model consisting of a CNN classifier similar to EfficientNet B3 architecture coupled with a support vector machine (SVM). The sample dataset containing four classes i.e., acne, atopic dermatitis, bullous disease, and eczema is a subset of the DermNet dataset. The proposed model is not only lightweight but also achieves better validation accuracy when compared to similar methods in its category.

Electronic Properties of ZnO Nanowires: A First-Principles Analysis Using Two-Probe Methodology

In this study, the electrical characteristics of pure ZnO nanowires are investigated using density functional theory (DFT) inside the non-equilibrium Green's function (NEGF) paradigm. We clarify the underlying electronic behaviours by a thorough analysis of I-V curves, density of states (DOS) spectra, and transmission spectra. All calculations are carefully carried out using the Quantum Wise Atomistic Tool Kit programme and the Generalised Gradient Approximation (GGA). Our results show that localised d orbitals dominantly contribute to ZnO nanowire transmission properties. We also investigate the effect of doping with copper on these characteristics. Gradual doping from pristine to 2 and 4 Cu atoms shows significant enhancements, indicating a clear relationship between electrical properties and doping concentration. Notably, the device's potential as a resistor is highlighted by the greater linearity of current with copper doping, demonstrating adherence to Ohm's law. Moving beyond basic research, we tackle real-world issues in sensing applications, including the quick identification of copper molecules in water. We provide light on the Au-ZnO-Au structure's potential for sensor applications by thoroughly discussing its electron transport properties. In conclusion, this study advances our knowledge of the electrical characteristics of ZnO nanowires and how doping modifies them, opening the door to their application in a variety of electronic and sensing devices.

Mo-doped carbon-dots nanozyme with peroxide-like activity for sensitive and selective smartphone-assisted colorimetric S2− ion detection and antibacterial application

Sulfur ion (S2−) plays a significant and considerable role in many living organisms and ecosystems, while its abnormal content can pose a serious hazard to human health and ecological environment. Hence, it is extremely meaningful to construct a highly sensitive and selective analytical platform for S2− detection in complex microenvironment, particularly in biological systems. In this study, phosphomolybdic acid and L-Arg were utilized to prepare a new molybdenum doped carbon-dots nanozyme (Mo-CDs) with great peroxidase-like activity by one-step hydrothermal approach. In the presence of H2O2, Mo-CDs converted 3, 3′, 5, 5′-tetramethyl benzidine (TMB) into blue oxTMB, but S2− strongly reduced the blue solution to colorless and then brown, which established significant selectivity toward S2−. Mo-CDs illustrated a wide linear range (2.5 μM-900 μM) and low detection limit (LOD = 76 nM) by ultraviolet and smartphone-assisted visualized colorimetric analysis. Especially, the smartphone-assisted analysis platform successfully realized quick, portable, sensitive and visible identification of S2− with high recovery (95.7–106.7 %) and excellent specificity in water samples. More importantly, Mo-CDs was developed to antibacterial applications based on good peroxidase-like activity. This research not only constructed a new and efficient carbon-dots nanozyme and a low-cost, portable, visual analysis platform for real-time detection of S2−, but also proposed a novel design strategy and methodology for exploiting multifunctional nanozyme detection tool with great practical application.

Versatile self-assembled near-infrared SERS nanoprobes for multidrug-resistant bacterial infection-specific surveillance and therapy

The rise of multidrug-resistant bacteria (MDRB) has made bacterial infection one of the biggest health threats, causing numerous antibiotics to fail. Real-time monitoring of bacterial disease treatment efficacy at the infection site is required. Herein, we report a versatile Raman tag 3,3′-diethylthiatricarbocyanine iodide (DTTC)-conjugated star-shaped Au-MoS2@hyaluronic acid (AMD@HA) nanocomposite as a surface-enhanced Raman scattering (SERS) nanoprobe for quick bacterial identification and in-situ eradication. Localized surface plasmon resonance (LSPR) from the hybrid metallic nanostructure makes AMD@HA highly responsive to the near-infrared laser, enabling it to demonstrate a photothermal (PTT) effect, increased SERS activity, and peroxidase-like catalytic reaction to release reactive oxygen species. The tail vein injection of AMD@HA nanoprobes is invasive, however SERS imaging for bacterial identification is non-invasive and sensitive, making it an efficient residual bacteria monitoring method. The detection limit for methicillin-resistant Staphylococcus aureus (MRSA) is as low as 102 CFU·mL-1, and the substrates allow for taking 120 s to acquire a Raman image of 1600 (40 × 40) pixels. In mouse models of MRSA-induced wound infection and skin abscess, the combination of AMD@HA-mediated PTT and catalytic therapy demonstrates a synergistic effect in promoting wound healing through rapid sterilization. This SERS-guided therapeutic approach exhibits little toxicity and does not cause considerable collateral damage, offering a highly promising intervention for treating diseases caused by MDRB. Statement of significanceThis research introduces a SERS nanoprobe, AMD@HA, for the rapid identification and eradication of multidrug-resistant bacteria (MDRB), a critical health threat. The nanoprobe leverages localized surface plasmon resonance for photothermal therapy and enhanced Raman signals, offering a sensitive, non-invasive diagnostic tool. With a low detection limit for MRSA and a synergistic therapeutic effect in mouse models, our approach holds significant promise for treating MDRB-driven infections with minimal toxicity, advancing the field of antimicrobial strategies.

Application of Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry for Identification of Foodborne Pathogens: Current Developments and Future Trends.

Foodborne pathogens have gained sustained public attention, exerted significant pressure on food manufacturers, and posed serious health risks to human. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been employed for quick and accurate identification of microorganisms in the prevention of foodborne epidemics in recent years. Herein, we first summarize the principle of MALDI and its workflow for foodborne pathogens. Subsequently, we review the recent progress and applications of MALDI-TOF MS in foodborne pathogen determination. Additionally, we outline the expanded utilization of MALDI-based techniques for the identification of closely related species. We also assess the current gaps and propose possible solutions to address the existing challenges. MALDI-TOF MS is a promising biotool for rapid and accurate identification of foodborne microbes at the species and genus level in food samples. Database expansion and direct quantification of spoilage microbes are two promising areas for future progress in MALDI-TOF MS applications.

Improvement of direct microscopy method for differential diagnosis of skin mycoses

Background and Aim.In the practice of veterinary doctors, a number of methods for diagnosing dermatomycoses are used, methods of direct and luminescent microscopy are undeservedly ignored. Сomparative analysis of staining methods for improving the method of direct microscopy, rapid and reliable detection of spores and mycelium in biomaterial, identification of pathogens and differential diagnosis of skin mycoses is of crucial importance for the effective treatment of animalsMaterials and Methods. 47 clinical samples of wool and skin taken from farm animals were examined for dermatomycetes. Diagnosis was performed using direct microscopy, fluorescence microscopy with calcofluor, and culture medium culture Saburo. Results. During the study of 47 samples of biological material, it was found that during microscopy the pathogen was detected in 10 cases (21.3%), during KOH microscopy - in 15 cases (29.9%), during KOH microscopy with other dyes in 11 cases (23.4%), during KOH microscopy with color calcofluor in 25 cases (53.2%). The efficiency of the KOH microscopy method with white calcofluor in comparison with direct microscopy was 60% higher, in comparison with KOH microscopy with other dyes - by 56%, classical KOH microscopy by 43.8%. By the culture method, pathogens were detected in 28 cases (59.6%), of which 7 Trichophyton spp., 1 Microsporum spp., 14 Aspergillius spp., 6 yeast. Growth was absent in 19 samples (40.4%), bacteria were detected in 3 samples (6.3%). In comparison with КOH microscopy with calcofluor white, positive results were confirmed in 89.3%.Conclusion. The use of coloring with various dyes of morphological elements of micromycetes in pure cultures allows you to identify mycelial hyphae of various thicknesses, spores, conidia, other morphological structures and carry out a quick preliminary identification of dermatomycetes and opportunistic mold fungi in smears.

DESIGNING A MICROSERVICES BASED ENTERPRISE ARCHITECTURE USING TOGAF 10: A CASE STUDY APPROACH

PT Umbara Karya Sejati, operating in the home living and industrial product sectors, faced significant digital transformation challenges, marked by inefficiencies due to system integration issues and vendor dependencies that incurred cost penalties of 10-15% each month. Addressing these challenges, this research developed an Enterprise Architecture using TOGAF 10's Microservices Architecture (MSA) from the Preliminary Phase to Phase F: Migration Planning. This approach aimed to enhance system integration and operational efficiency, thereby improving modularity and scalability while reducing reliance on external vendors. A pivotal component of this architecture, an API gateway, provided robust monitoring capabilities for integration processes, enabling quick identification and prioritization of critical issues, which will assist the internal IT team's workflow. Furthermore, the research planned the establishment of a DevOps team, incorporating Agile methodologies, and scheduled IT governance and data security training to prepare for future policy development and strengthen internal controls. This strategic design equips the organization to navigate the volatility, uncertainty, complexity, and ambiguity (VUCA) of market demands with agile and effective responses, projecting a 10% increase in both operational and cost efficiency for PT Umbara Karya Sejati.

Description of three widespread new Peronospora species parasitising Caryophyllales

Downy mildews are cosmopolitan obligate biotrophic parasites of flowering plants. They are fungus-like eukaryotes of the kingdom Straminipila, and most species are highly specialised, often parasitising only a single host species. Due to the prevalence of a broad species concept before the advent of molecular phylogenetics and the paucity of differentiating characteristics in some groups, many widespread species are still undescribed. This is also the case for some downy mildews in the clade containing the type species of Peronospora, Peronospora rumicis. Spurred by the discovery of the undescribed species parasitising Rumex acetosa at the Blávík research station in East Iceland, three new widespread species of Peronospora are introduced in this article: Peronospora blauvikensis parasitic to Rumex acetosa, P. boylei parasitic to Rumex thyrsiflorus, and P. spergulariae parasitic to Spergula marina. As the species are morphologically highly similar, a set of diagnostic bases is given for each species, allowing a quick identification via molecular barcoding.