Analysis Platform Research Articles

An Integrated Paradigm for Advanced Irrigation Systems Leveraging Internet of Things (IoT)

As the demand for precision in agriculture and effective sustainable resource management increases, there has been a lot of pressure to have more accurate and efficient soil moisture data-transmission systems. This paper includes the discussion on how machine learning (ML) and deep learning (DL) techniques can be improvise to further accuracy and performance in IoT-based smart irrigation systems. The system is based on the perception of soil moisture using IoT sensors collecting real-time environmental data in fields, such as soil moisture, temperature, humidity, and sunlight, to be transmitted to farmers or end-users using ThingSpeak and Thinger.io platforms for analysis, storage, and visualization. It enables real-time decisions and remote agricultural systems control using web page or mobile applications. The WSNs(Wireless sensor network) helps to automate irrigation and water management in agricultural sites. The developed SIS(Smart Irrigation Systems) is based on the sensor network that carries real-time information relating to moisture content in the soil, temperature, and humidity levels as very critical determinants of the proper irrigation schedule. These are analyzed every 15 minutes at the edge server. The system uses deep learning models to predict when the soil moisture falls below a threshold and automatically activates water pumps or sprinklers, which reduces human intervention and optimizes water usage in agriculture. An important part of research forms machine learning algorithms to improvise the performance. The recent advancements relies on several models, among them KNN (K-Nearest neighbors) and TimeGPT models [TimeGPT is a time-series forecasting model that utilizes the power of GPT (Generative Pre-trained Transformer) architecture to predict future values in a sequence.], in the prediction of soil moisture while achieving optimal irrigation schedules from previous available data and weather forecasting. The comparative analyses included an accuracy rate of 97 Parsant to 98 Parsant in KNN thus describing the high level of accuracy that the system can attain regarding the soil conditions and water requirements. This research provides the possibility of embedding IoT into machine learning that may form a new age smart agriculture system that, apart from irrigation automation, enhances decision-making in farmer practices. Adoption of such systems could contribute immensely to the sustainability of such agricultural practices mainly due to reduced water wastage, decrease operation costs, and improved crop yield. Integrating real-time environmental data and predictive analytics farmlands can be optimized and maintained even the land has water scarcity or difficult to receive rainfall or other climatic conditions.

Utilizing Raspberry Pi and Internet of Things (IoT) Frameworks for Comprehensive Monitoring of Urban Pollutants and Climate Variables

Global warming is the effect of a rise in Earth's climate temperature due to air pollution. Urban areas have higher levels of air pollution than rural areas due to increased traffic and rapid development. In addition to brief health problems like headaches, eye infections, and throat in-factions, pollution also has lengthy health repercussions like lung cancer and heart disease. As a result, it's important to keep an eye on the many climatic and pollution indicators, including light intensity, temperature, humidity, air pressure, oxygen and carbon dioxide level and camera. Using suitable equipment and Internet of Things (IoT) technologies, a pollution and climate monitoring system is constructed in the proposed paper that can measure the parameters above at regular intervals. The system then uploads the data to a webpage in the Thing Speak. The IoT analytics platform service Thing Speak enables the collection, visualization, and analysis of real-time data streams. The sensors are used to feed data to Thing Speak, which instantly visualizes the data in a graph. The main tool for gathering data from the sensors and sending parameters to the website is the Raspberry Pi3 computer. The software code is developed using Python. By logging on to the website from any location in the world, one can view the submitted pollution and climate parameters of a specific location.

Comparison of different microbiome analysis pipelines to validate their reproducibility of gastric mucosal microbiome composition.

Microbiome analysis has become a crucial tool for basic and translational research due to its potential for translation into clinical practice. However, there is ongoing controversy regarding the comparability of different bioinformatic analysis platforms and a lack of recognized standards, which might have an impact on the translational potential of results. This study investigates how the performance of different microbiome analysis platforms impacts the final results of mucosal microbiome signatures. Across five independent research groups, we compared three distinct and frequently used microbiome analysis bioinformatic packages (DADA2, MOTHUR, and QIIME2) on the same subset of fastQ files. The source data set encompassed 16S rRNA gene raw sequencing data (V1-V2) from gastric biopsy samples of clinically well-defined gastric cancer (GC) patients (n = 40; with and without Helicobacter pylori [H. pylori] infection) and controls (n = 39, with and without H. pylori infection). Independent of the applied protocol, H. pylori status, microbial diversity and relative bacterial abundance were reproducible across all platforms, although differences in performance were detected. Furthermore, alignment of the filtered sequences to the old and new taxonomic databases (i.e., Ribosomal Database Project, Greengenes, and SILVA) had only a limited impact on the taxonomic assignment and thus on global analytical outcomes. Taken together, our results clearly demonstrate that different microbiome analysis approaches from independent expert groups generate comparable results when applied to the same data set. This is crucial for interpreting respective studies and underscores the broader applicability of microbiome analysis in clinical research, provided that robust pipelines are utilized and thoroughly documented to ensure reproducibility.IMPORTANCEMicrobiome analysis is one of the most important tools for basic and translational research due to its potential for translation into clinical practice. However, there is an ongoing controversy about the comparability of different bioinformatic analysis platforms and a lack of recognized standards. In this study, we investigate how the performance of different microbiome analysis platforms affects the final results of mucosal microbiome signatures. Five independent research groups used three different and commonly used bioinformatics packages for microbiome analysis on the same data set and compared the results. This data set included microbiome sequencing data from gastric biopsy samples of GC patients. Regardless of the protocol used, Helicobacter pylori status, microbial diversity, and relative bacterial abundance were reproducible across all platforms. The results show that different microbiome analysis approaches provide comparable results. This is crucial for the interpretation of corresponding studies and underlines the broader applicability of microbiome analysis.

A United Kingdom-wide audit of the laboratory investigation of primary aldosteronism.

Primary Aldosteronism (PA) is a common but under-recognised cause of secondary hypertension. Early diagnosis with targeted medical and/or surgical intervention is important to prevent irreversible end-organ damage. An Endocrine Society Clinical Practice Guideline was used to define audit standards against which to assess current United Kingdom (UK) laboratory practice. A survey comprising 22 questions, which captured information on screening, confirmatory testing and adrenal vein sampling (AVS), was distributed to all UK Clinical Biochemistry laboratories by the Association for Laboratory Medicine. Consultation with clinical colleagues was encouraged. 50 of 147 laboratories (34.0%) responded, 17 of which provided an analytical service for plasma aldosterone concentration (PAC) and renin, measured as plasma renin activity (PRA) or direct renin concentration (DRC). PRA/DRC, PAC and aldosterone:renin ratios were used to screen for PA. Saline infusion testing was the most common confirmatory test. AVS was used to aid lateralisation. Chemiluminescence immunoassay and liquid chromatography tandem-mass spectrometry were the preferred analytical methods for PAC and PRA/DRC. However, there was considerable variation across centres in respect of reference intervals and cutoffs, which were not fully accounted for by differences in analytical platforms. Although diagnostic algorithms, with pre- and post-analytical support, were in evidence in some centres, these were not universal or always embedded in a multidisciplinary team setting. We observed significant heterogeneity in the laboratory investigation of PA across the UK. Therefore, this work serves as a stimulus for greater collaboration to permit national harmonisation/standardisation of analytical and clinical aspects of UK PA practice.

PhysioEx, a new Python library for explainable sleep staging through deep learning.


Sleep staging is a crucial task in clinical and research contexts for diagnosing and understanding sleep disorders. This work introduces PhysioEx, a Python library designed to support the analysis of sleep stages using deep learning and Explainable AI (XAI). 

Approach:
PhysioEx provides an extensible and modular API for standardizing and automating the sleep staging pipeline, covering data preprocessing, model training, testing, fine-tuning, and explainability. It supports both low-resource devices and high-performance computing clusters and includes pretrained models based on the Sleep Heart Health Study (SHHS) dataset. These models support single-channel EEG and multichannel EEG-EOG-EMG configurations and are easily adaptable to custom datasets. PhysioEx also features a command-line interface toolbox allowing users to streamline the model development and deployment. The library offers a range of XAI post-hoc methods to explain model decisions and align them with expert knowledge. 

Main results:
PhysioEx benchmark state-of-the-art sleep staging models in a standard pipeline. Enabling a fair comparison between them both on the training source and out-of-domain sources. Its XAI techniques provide insights into deep learning-based sleep staging by linking model decisions to human-understandable concepts, such as AASM-defined rules. 

Significance:
PhysioEx addresses the need for a standardized and accessible platform for sleep staging analysis, combining deep learning and XAI. By supporting modular workflows and explainable insights, it bridges the gap between machine learning models and clinical expertise. PhysioEx is publicly available and installable via pip, making it a valuable tool for researchers and practitioners in sleep medicine.

Plasmonic Slippery Surface for Surface-Enhanced Raman Spectroscopy and Protein Adsorption Inhibition.

Slippery liquid-infused porous surfaces (SLIPSs) are a class of surface that offers low contact angle hysteresis and low tilt angle for water droplet shedding. This property also endows the surface with pinning-free evaporation, which in turn has been exploited for analyte concentration enrichment for Surface Enhanced Raman Spectroscopic applications and antibiofouling. Herein, we demonstrate a facile approach for creating SLIPS with low contact angle hysteresis and low tilt angle for water shedding by coating the equal-volume mixture of polydimethylsiloxane (PDMS) and silicone oil. By exploiting the in situ plasmonic particle reduction capability of the PDMS, the surface is converted to plasmonic SLIPS, which illustrates its potential as a sensitive analytical platform via surface-enhanced Raman spectroscopy. The Raman spectroscopic studies using crystal violet as a reference sample show a limit of detection of 76 pM. Further, we have demonstrated that the fabricated plasmonic substrate is found to be more efficient in inhibiting proteins (bovine serum albumin) on the surface compared to pristine PDMS surfaces. Our fabricated plasmonic surface can find applications in ultrasensitive molecular detection for applications related to analytical chemistry, diagnostics, environmental monitoring, and national security and more importantly can control the nonspecific adsorption of proteins.

Metabolic Fingerprint of Dual Body Fluids Deciphers Diabetic Retinopathy.

Diabetic retinopathy (DR) is a microvascular complication of diabetes, affecting 34.6% of diabetes patients worldwide. Early detection and timely treatment can effectively improve the prognosis of DR. Metabolomic analysis provides a powerful tool for studying pathophysiological processes. Conducting metabolomic analyses on DR-related biofluids helps identify differential metabolic expressions during disease progression, thereby discovering potential biomarkers to support clinical diagnosis and treatment. Here, an innovative workflow for vitreous liquid analysis is established, and a machine learning-based DR analysis platform integrating vitreous liquid metabolic fingerprint (VL-MF) and plasma metabolic fingerprint (P-MF) derived via nanoparticle enhanced laser desorption/ionization mass spectrometry is developed. Direct VL-MF and P-MF are obtained with desirable reproducibility (coefficient of variation, CV <5%) and remarkable speed (3s per sample), and DR patients are distinguished from healthy controls applying dual biofluid-MF with an area under the curve (AUC) of 0.957. Moreover, a biomarker candidate panel from vitreous liquid and plasma with an AUC of 0.945 is constructed and the related metabolic pathways are identified by metabolomics pathway analysis (MetPA). This work offers a powerful multi-biofluid platform that can not only contribute to DR but also provide solid references for other clinical applications.

Boosting human immunology: harnessing the potential of immune organoids.

Studying the human immune system in vivo is challenging and often not possible. Therefore, most human immunology studies have been predominantly confined to peripheral blood analyses, which by themselves have inherent limitations, as many immune reactions take place within tissues. For example, potent antibody responses that contribute to fighting infections and provide protection following vaccination require cellular interactions between B cells and T cells in specialized micro-anatomical structures called germinal centers, which are found in secondary lymphoid organs such as spleen, lymph nodes, and tonsils. Thus, there is a clear demand for novel enhanced experimental systems that faithfully recapitulate the intricate dynamics of the human immune system as much as possible. In this review, we discuss recent advances in versatile human tonsil/adenoid tissue-based ex vivo immune organoid cultures as well as related cancer and autoimmunity-focused experimental setups. These systems have been implemented as translational immunology platforms for in-depth analyses of human B and T cell-mediated immune responses, thereby facilitating mechanistic studies as well as drug and vaccine testing in a human-first approach.

Construction of an Intelligent Medical Monitoring System Network for Exercise Rehabilitation Based on the Internet of Things

Exercise rehabilitation refers to a rehabilitation method that restores or improves human motor function through scientific, systematic, and regular exercise training, and it has a very wide range of applications. This paper explored the construction of an intelligent medical monitoring system network for sports rehabilitation based on the Internet of Things (IoT) technology. The system consists of a wireless sensor acquisition module for collecting sports rehabilitation data, a data storage and analysis module for cloud management and deep learning analysis, and a medical monitoring module for remote supervision and patient interaction. The system aimed to achieve personalized exercise rehabilitation plans through real-time monitoring and data collection of patients, and provide remote medical monitoring services to improve the rehabilitation effect and quality of life of patients. The system architecture adopted the IoT technology and cloud storage technology, and combined the deep learning convolutional neural network (CNN) model to achieve remote monitoring and data analysis. The monitoring center of the system can monitor patients’ physiological indicators, exercise status, and rehabilitation effects in real time through IoT wireless sensors, and upload the data to the cloud platform for storage and analysis. Doctors and patients can access data through mobile phones or computers and communicate online. The data showed that when measuring a patient’s heart rate using wireless sensors, the measurement accuracy mostly exceeded 99.8%, and some samples had detection accuracy up to 100%. In addition, traditional sensors can detect heart rates up to 5.1[Formula: see text]s, while wireless sensors can detect heart rates up to 1.28[Formula: see text]s. The CNN had a maximum recall rate of 99.51%, a precision of 99.68%, and an accuracy rate of 99.8% in the classification of psychological data of rehabilitation patients. The experimental results indicated that the system can achieve comprehensive monitoring and effective management of the patient’s rehabilitation process, and its operation is stable and has good practical value.

Exploring the Mechanism of Shi-San-Wei-He-Zhong-Wan in the Treatment of Functional Dyspepsia Based on Network Pharmacology and Experimental Validation.

The incidence of Functional Dyspepsia (FD) is gradually increasing, yet there are currently no effective treatment methods available. This study explored the effective components, potential targets, and pathways of Shi-San-Wei-He-Zhong-Wan (SSWHZW) in the treatment of FD, aiming to provide new insights into its treatment. First, the Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP) and GeneCards databases were utilized to identify the major active components of SSWHZW and potential therapeutic targets of FD. Subsequently, functional enrichment analyses were performed to elucidate the mechanisms of SSWHZW on FD. Molecular docking simulations were then conducted to assess the binding affinity of key targets and major active components. Next, an FD animal model was established, and the therapeutic effects of SSWHZW were validated using Hematoxylin and Eosin (HE) staining and Enzyme-linked Immunosorbent Assay (ELISA). Finally, Western blot analysis was performed to validate the involvement of key signaling pathways. A total of 229 active ingredients and 283 putative targets were identified from SSWHZW, of which 173 overlapped with the targets of FD and were considered potential therapeutic targets. Key ingredients, such as quercetin, kaempferol, wogonin, and baicalein, were identified as pivotal components of SSWHZW, potentially acting on the 173 overlapping targets and influencing FD through related signaling pathways. Functional enrichment analysis revealed that the PI3K-Akt signaling pathway, VEGF signaling pathway, and NF-kappa B signaling pathway may be involved in the mechanism of SSWHZW in treating FD. Molecular docking predicted that all five ingredients could firmly bind with the top-ranked target TP53 in the Protein- protein Interaction (PPI) network. Further experiments demonstrated that SSWHZW protected the intestinal tissues of FD rats from inflammatory damage by inhibiting the PI3K/AKT signaling pathway. Based on network pharmacology, this study explored the multi-component, multitarget, and multi-pathway characteristics of SSWHZW in treating FD. The findings suggest that SSWHZW exerts its anti-FD effects by inhibiting the expression of the PI3K/AKT signaling pathway, providing new insights and methods for further research on the mechanism of SSWHZW in treating FD.

Advancing DNA Structural Analysis: A SERS Approach Free from Citrate Interference Combined with Machine Learning.

Surface-enhanced Raman spectroscopy (SERS) has become an indispensable tool for biomolecular analysis, yet the detection of DNA signals remains hindered by spectral interference from citrate ions, which overlap with key DNA features. This study introduces an innovative, ultrasensitive SERS platform utilizing thiol-modified silver nanoparticles (Ag@SDCNPs) that overcomes this challenge by eliminating citrate interference. This platform enables direct, interference-free detection and structural characterization of a wide range of DNA conformations, including single-stranded DNA (ssDNA), double-stranded DNA (dsDNA), i-motif, hairpin, G-quadruplex, and triple-stranded DNA (tsDNA). Employing calcium ions as aggregating agents and deuterated methanol as an internal standard, the system achieved high spectral quality and reproducibility. Machine learning (ML) techniques, such as linear discriminant analysis (LDA) and t-distributed stochastic neighbor embedding (t-SNE), were utilized for spectral classification, alongside support vector machines (SVM) for predictive modeling, yielding accuracies above 99%. These findings establish a robust and versatile platform for DNA structural analysis, offering transformative potential for applications in clinical diagnostics and biomedical research.

2-Propanol Suspension Method to Increase Acetylcholinesterase and Flow Stability on μPADs.

Ensuring detection performance and shelf life is crucial for analytical devices. Advances in materials and reaction mechanisms have improved detection performance, yet extending the operational lifetime of microfluidic paper-based analytical devices (μPADs)─especially those reliant on sensitive enzymes─remains a challenge. Here, we present an alternative to air-drying and lyophilization: loading enzymes suspended in 2-propanol (iPrOH). By suspending the enzyme in iPrOH, we circumvent the enzyme activity losses commonly associated with freeze-thawing and freeze-drying. Accelerated aging tests, supported by statistical analyses of long-term activity retention (including comparisons over multiple time points), indicate that while conventional methods do not sustain consistent superiority, the iPrOH suspension method maintains higher enzymatic activity over extended periods. By avoiding stabilizers and circumventing the limitations of other techniques, our method enables μPADs to achieve both longevity and stable fluid flow. Thus, we provide a more robust, on-site analytical platform capable of reliable on-site detection.

Evaluation of the effect of the water-energy nexus on the performance of the water-energy supply system.

In this study, the water-energy nexus is investigated throughout coupling the Water Evaluation and Planning (WEAP) and Low Emission Analysis Platform (LEAP) models under the climate change effects in the Marun basin, Iran. For this purpose, first, the climate change effects on water resources and consumption nodes are calculated under representative concentration pathway (RCP) scenarios from the fifth report of the International Panel on Climate Change (IPCC). Artificial neural network (ANN) is used to model river inflow and Cropwat model is used for agricultural water demand in future time (2015-2040). In the next step, water system is modeled in WEAP and energy system is modeled in LEAP. Then, three water-energy nexus scenarios include (1) calculating the actual hydropower generation capacity in LEAP model (W-E-N1), (2) water-energy nexus evaluation indexes in the WEAP model (W-E-N2), and (3) changes in the electricity consumption intensity in the LEAP model (W-E-N3) are modeled under RCP 2.6, RCP 4.5 and RCP 8.5 climate scenarios. The results of W-E-N1 scenario show that the actual hydropower generation capacity with water-energy nexus is reduced by 78%, 89%, and 91%, respectively, compared to no water-energy nexus under RCP 2.6, RCP 4.5, and RCP 8.5 scenarios. The results of W-E-N2 show that the hydropower generation reliability index will be reduced by 19%, 31%, and 13%, respectively under RCP 2.6, RCP 4.5, and RCP 8.5 compared no water-energy nexus. The results of W-E-N3 scenario show that the electricity consumption intensity will increase considering the electricity required for water transferring to demand sites. The results of the present study show that the water-energy nexus will lead to more accurate results and, as a result, more realistic planning for water and energy resources.

Use of IoT with Deep Learning for Classification of Environment Sounds and Detection of Gases

The need for safe and healthy air quality has become critical as urbanization and industrialization increase, leading to health risks and environmental concerns. Gas leaks, particularly of gases like carbon monoxide, methane, and liquefied petroleum gas (LPG), pose significant dangers due to their flammability and toxicity. LPG, widely used in residential and industrial settings, is especially hazardous because it is colorless, odorless, and highly flammable, making undetected leaks an explosion risk. To mitigate these dangers, modern gas detection systems employ sensors, microcontrollers, and real-time monitoring to quickly identify dangerous gas levels. This study introduces an IoT-based system designed for comprehensive environmental monitoring, with a focus on detecting LPG and butane leaks. Using sensors like the MQ6 for gas detection, MQ135 for air quality, and DHT11 for temperature and humidity, the system, managed by an Arduino Mega, collects data and sends these to the ThingSpeak platform for analysis and visualization. In cases of elevated gas levels, it triggers an alarm and notifies the user through IFTTT. Additionally, the system includes a microphone and a CNN model for analyzing audio data, enabling a thorough environmental assessment by identifying specific sounds related to ongoing activities, reaching an accuracy of 96%.

ARTN and CCL23 predicted chemosensitivity in acute myeloid leukemia: an Olink® proteomics approach

BackgroundThe standard “7 + 3” induction results in 30% of de novo acute myeloid leukemia (AML) patients not achieving complete remission (CR). We aimed to utilize the Olink® platform to compare the bone marrow plasma proteomic profiles of newly diagnosed de novo AML patients who did and did not achieve CR following “7 + 3” induction treatment.MethodsThis prospective study included 43 untreated AML patients, stratified into CR (n = 29) and non-CR (n = 14) groups based on their response to “7 + 3” induction therapy. We employed the Olink® Explore-384 Inflammation platform for proteomic analysis to investigate differences in bone marrow plasma protein levels between the CR and non-CR groups.ResultsProteomic analysis demonstrated that the CR group exhibited significantly higher bone marrow plasma levels of ARTN and CCL23 than did the non-CR group. Immunohistochemical staining confirmed a higher proportion of tissue samples with intense staining for ARTN (25.40% vs. 7.05%, p = 0.013) and CCL23 (24.14% vs. 14.29%, p = 0.039) in the CR group. These findings were corroborated by bulk-RNA-seq, which indicated significantly elevated mRNA expression levels of ARTN (1.93 vs. -0.09; p = 0.003) and CCL23 (1.50 vs. 0.12; p = 0.021) in the CR group. The Human Protein Atlas provided external support for our findings.ConclusionsThe results suggest that ARTN and CCL23 may serve as biomarkers for predicting responsiveness to the “7 + 3” induction in untreated AML. Using an enzyme-linked immunosorbent assay to identify the roles of ARTN and CCL23 in predicting AML chemosensitivity may enhance clinical applicability in the future.

The Development and Optimisation of a Urinary Volatile Organic Compound Analytical Platform Using Gas Sensor Arrays for the Detection of Colorectal Cancer

The Development and Optimisation of a Urinary Volatile Organic Compound Analytical Platform Using Gas Sensor Arrays for the Detection of Colorectal Cancer

A shikimic acid derived carbon dots (SACNDs-FITC) for multi-modal detection and removal of Hg2+: Probe design, sensing performance, and applications in food analysis.

The first shikimic acid derived fluorescent carbon dots (SACNDs-FITC) for multi-modal detection and simultaneous removal of Hg2+ is revealed. The fluorescence of SACNDs-FITC centered at 520nm can be selectively quenched by Hg2+, while the emission centered at 420nm remains constant which can be used for self-calibration. Naked-eye distinguishable color change from yellow to colourless under daylight and from green to blue under UV light could be observed for SACNDs-FITC in the real-time detection of Hg2+. Novel approaches that combine smartphone automated color hue (RGB and HSV) readout for intelligent Hg2+ analysis was developed. Moreover, SACNDs-FITC could work as an effective absorbent for Hg2+ with an adsorption capacity of 19.24mg/g. Mechanism studies revealed the success of the post-modification strategy and the crucial role of -NCSN- groups in detecting and adsorbing Hg2+. Most importantly, multi-modal analysis of Hg2+ in celery cabbage and apples was performed using this analytical platform.

Stability study of topotecan in ophthalmic prefilled syringes for intravitreal delivery

BackgroundIntravitreal and intracameral administration of melphalan and topotecan (TPT) has shown its efficacy in the treatment of retinoblastoma over the last few years. Due to rapid hydrolysis, melphalan must be...

Building Modern Banking Solutions: A Technical Guide to .NET Implementation in U.S. Financial Services

This comprehensive technical article explores the implementation of .NET technology in modern U.S. banking systems, focusing on the digital transformation of financial services. The article examines how financial institutions leverage .NET's robust framework to develop scalable, secure, and efficient banking solutions. It investigates core banking architecture, essential product development, cloud integration strategies, security frameworks, and future technological considerations. It demonstrates how .NET's cross-platform capabilities and extensive ecosystem enable banks to create sophisticated applications while maintaining regulatory compliance. The analysis encompasses various aspects of banking technology, including payment processing systems, fraud detection mechanisms, customer analytics platforms, and cloud-based solutions. The article also addresses the critical role of security and regulatory compliance in modern banking systems, highlighting how institutions implement comprehensive security frameworks and automate compliance processes. Furthermore, it explores the evolution of development tools and future considerations, emphasizing the importance of API-first strategies, microservices architecture, and sustainable maintenance planning in the banking sector.

Innovative Infrared Spectroscopic Technologies for the Prediction of Deoxynivalenol in Wheat.

Mycotoxin contamination in cereals is a global food safety concern. One of the most common mycotoxins in grains is deoxynivalenol (DON), a secondary metabolite produced by the fungiFusarium graminearum and Fusarium culmorum. Exposure to DON can lead to adverse health effects in both humans and animals including vomiting, dizziness, and fever. Hence, the development of analytical technologies capable of predicting mycotoxin contamination levels in grains is crucial. In this study, we emphasize innovative infrared (IR) spectroscopic technologies for the prediction of DON in wheat along the food supply chain. The performance of an IR laser spectroscopic platform for on-site or laboratory confirmative analysis was evaluated. Furthermore, the performance of a handheld IR spectrometer for preliminary screening during transportation, storage, or harvesting was assessed. The accuracy of cross validation (AccCV) obtained with the laser spectrometer reached 92%, while the handheld IR spectrometer achieved 84.6%. Hence, both technologies prove significant potential for rapid mycotoxin detection.