Web-based Interface Research Articles

Smart Monitoring and Control System for Crude Oil Pipeline Vandalism

Crude oil pipeline vandalism, theft, and unauthorized interference present a significant challenge to the oil industry's security, profitability, and sustainability. Despite initial measures such as security patrols, traditional methods have proven inadequate, particularly in difficult terrains and aquatic regions. These methods often fail to promptly detect and prevent vandalism and theft, which results in significant economic, environmental, and safety risks. This study developed and implemented a smart monitoring and control system to address these challenges by improving the detection of oil pipeline vandalism, including incidents in aquatic environments. The Prototyping methodology was adopted, allowing for continuous testing and improvements throughout the development process. The system was built using C#.NET under the .NET Framework, with Microsoft SQL Server as the database, incorporating an encryption layer for database security. To enhance its effectiveness, an oil theft detection algorithm was integrated with a web-based interface to facilitate real-time monitoring, visualization, and reporting. Testing scenarios demonstrated that the system successfully detected threats and ensured seamless data flow from sensors to the central system. This solution marks a significant advancement in the security and sustainability of oil infrastructure, contributing to environmental protection and the safety of stakeholders

The infection post flexible UreteroreNoscopy (I-FUN) predictive model based on machine learning: a new clinical tool to assess the risk of sepsis post retrograde intrarenal surgery for kidney stone disease.

To create a machine-learning model for estimating the likelihood of post-retrograde intrarenal surgery (RIRS) sepsis. All consecutive patients with kidney stone(s) only undergoing RIRS in 16 centers were prospectively included (January 2022-August 2023). adult, renal stone(s) only, CT scan (within three months), mid-stream urine culture (within 10 days). concomitant ureteral stone, bilateral procedures. In case of symptomatic infection/asymptomatic bacteriuria, patients were given six days of antibiotics according to susceptibility profiles. All patients had antibiotics prophylaxis. Variables selected for the model: age, gender, age-adjusted Charlson Comorbidity Index, stone volume, indwelling preoperative bladder catheter, urine culture, single/multiple stones, indwelling preoperative stent/nephrostomy, ureteric access sheath, surgical time. Analysis was conducted using Python programming language, with Pandas library and machine learning models implemented using the Scikit-learn library. Machine learning algorithms tested: Decision Tree, Random Forest, Gradient Boosting. Overall performance was accurately estimated by K-Fold cross-validation with three folds. 1552 patients were included. There were 20 (1.3%) sepsis cases, 16 (1.0%) septic shock cases, and three more cases (0.2%) of sepsis-related deaths. Random Forest model showed the best performance (precision = 1.00; recall = 0.86; F1 score = 0.92; accuracy = 0.92). A web-based interface of the predictive model was built and is available at https://emabal.pythonanywhere.com/ CONCLUSIONS: Our model can predict post-RIRS sepsis with high accuracy and might facilitate patient selection for day-surgery procedures and identify patients at higher risk of sepsis who deserve extreme attention for prompt identification and treatment.

StaR-LIF: State-resolved laser-induced fluorescence modeling for diatomic molecules

This study introduced a new model for quantitative analysis of the state-resolved laser-induced fluorescence signal of diatomic molecules, namely StaR-LIF. This model is built upon a master equation of the collisional-radiative transfer processes, which incorporated the latest data on the collisional energy transfer rates between individual spin- and parity-resolved rovibronic quantum levels, together with the most recent updates on the energy levels, line strengths and broadening/shift parameters of the relevant absorption and fluorescence transitions. To facilitate the use of this model, a web-based graphic user interface has been developed and made available at https://starlif.pku.edu.cn. Example applications of the current model have been demonstrated for NO, OH and CH, including parametric studies on the effects of variable pulse width and saturating excitation, as well as the temporal evolution of fluorescence spectrum during collisional transfer. The StaR-LIF model can provide quantitative modeling and analysis capabilities over a wide range of gasdynamic and excitation conditions, and promises to be useful in future LIF studies of complex reacting flows.

Artificial intelligence in cardiology: a machine learning model for supraventricular tachycardia discrimination

Abstract Background Distinguishing atrioventricular nodal reentrant tachycardia (AVNRT) from orthodromic atrioventricular reentrant tachycardia (AVRT) presents a diagnostic challenge, with clinical and electrocardiogram (ECG) features playing a crucial role in differential diagnosis. Purpose The aim of this study was to devise and validate a predictive algorithm that utilises basic clinical and ECG parameters to accurately classify AVNRT versus AVRT. Furthermore, this algorithm was integrated into a user-friendly, web-based tool to support clinical decision-making. Methods We conducted a prospective analysis of 704 patients (median age: 53.6 years; 59.7% female) with electrophysiologically confirmed AVNRT (n=535) or AVRT (n=169). A comprehensive evaluation was performed using a structured questionnaire tailored to arrhythmia-specific symptoms. The study utilised advanced machine learning techniques for classification, employing feature selection methods to enhance predictive accuracy. The analysis included 19 demographic, clinical, and ECG variables, adopting an 80-20% split for training and internal validation. The algorithm's performance was evaluated against a model comprising all variables (Full Model), expert ECG interpretations (ExpertECG), objective ECG criteria (ECG model), and a baseline dummy classifier, with efficacy assessed by the Area Under the Receiver Operating Characteristic curve (AUC) and the Area Under the Precision-Recall Curve (AUPRC). Results The optimised model, utilising the XGBoost algorithm with six critical variables (initial age at first episode, biological sex, presence of neck palpitations, pseudoR wave in lead V1/pseudoS wave in inferior leads, visibility of retrograde P-waves, and tachycardia cycle length), exhibited superior performance, achieving an AUC of 0.916 and an AUPRC of 0.823. This model significantly outperformed other comparative models in terms of predictive accuracy (Figure 1). Implementation of the model into a web-based interface was accomplished using Shiny, enhancing its accessibility for clinical application (Figure 2). Conclusions This study illustrates the efficacy of employing machine learning algorithms to utilise bedside clinical and ECG variables for accurately differentiating between AVNRT and AVRT. The creation of a web-based calculator marks a significant advancement, empowering clinicians, especially non-specialists, with a potent tool for providing personalised diagnostic insights. Future research should focus on external validation to ascertain the model's generalisability and its applicability in broader clinical settings.Figure 1Figure 2

Decentralizing Democracy: Secure and Transparent E-Voting Systems with Blockchain Technology in the Context of Palestine

Elections and voting play a crucial role in the development of a democratic society, enabling the public to express their views and participate in the decision-making process. Voting methods have evolved from paper ballot systems to e-voting systems to preserve the integrity of votes, ensuring a secure, transparent, and verifiable process. Continuous efforts have been made to develop a secure e-voting system that eliminates fraud attempts and provides accurate voting results. In this paper, we propose the architecture of a blockchain-based e-voting system called VoteChain. Developed to support the existing voting system in the state of Palestine, VoteChain aims to provide secure e-voting with features such as auditability, verifiability, accuracy, privacy, flexibility, transparency, mobility, availability, convenience, data integrity, and distribution of authority. The work introduces a smart contract designed to meet the demands of e-voting, governing transactions, monitoring computations, enforcing acceptable usage policies, and managing data usage after transmission. The proposed system also adopts advanced cryptographic techniques to enhance security. VoteChain features a web-based interface to facilitate user interaction, providing protection against multiple or double voting to ensure the integrity of the election. Furthermore, VoteChain is designed with a user-friendly and easily accessible administrator interface for managing voters, constituencies, and candidates. It ensures equal participation rights for all voters, fostering fair and healthy competition among candidates while preserving voter anonymity. A comparative analysis demonstrates VoteChain’s advancements in privacy, security, and scalability over both traditional and blockchain-based e-voting systems.

Otomatisasi Penyemprotan Polyester Menggunakan Kawasaki Cobot dengan Human Machine Interface (HMI) Berbasis Web

Brago Luchttechniek BV is a company specializing in the production of PIR (Polyisocyanurate) air ducts, facing challenges in maintaining accuracy and consistency in the polyester insulation spraying process on its products using Kawasaki collaborative robots (cobot) for automation. To address these challenges, this study designed a web-based Human Machine Interface (HMI) to automate the polyester insulation spraying process on PIR air ducts using the Kawasaki Cobot. A web-based HMI was chosen for its ease of development, flexibility, and integration capabilities with modern technology. This HMI uses the TCP/IP protocol to send real-time command signals to automation devices such as the Kawasaki Cobot and devices connected to the Programmable Logic Controller (PLC). Testing results demonstrated an improvement in the spraying process efficiency, reducing the time from 15–18 minutes conventionally to 5–7 minutes with the web-based HMI automation. Additionally, the web-based HMI successfully communicated with other devices connected to the robot, such as the conveyor belt and rotating table, via TCP/IP protocols, with each device having its own ID and IP address to facilitate linear message delivery, prevent collisions, and minimize errors. After eight successful automation tests on four different products, the system operated smoothly and systematically through the available HMI controls. The system was also evaluated by four respondents, with an overall score of 4.33 out of 5, indicating a high level of satisfaction.

GRASSIUS 2.0: A gene regulatory information knowledgebase for maize and other grasses

Grass species, which include the major cereal crops maize, wheat, rice, and sugarcane, are an integral part of our global agriculture and source of food and energy for a growing world population. GRASSIUS was established as a knowledgebase for transcription factors (TFs) and coregulators (CoRegs) in maize and several other species in the grass family. TFs are a primary component of the gene regulatory networks (GRNs) and the underlying gene regulatory grids (GRGs) that govern all aspects of plant growth and metabolism. GRASSIUS is the source for all information pertaining to the maize TFome collection, which serves as a powerful resource for the discovery of GRNs in maize and other cereals [1,2]. Here we describe the release of the GRASSIUS 2.0 knowledgebase (www.grassius.org) with updated data, query, and tool features, as well as the ability to expand to accommodate future datasets. The membership and annotation of all TF and CoReg families has been updated and revised to include gene models from v3, v4, and v5, of the maize B73 genome and recent genome versions of rice, sorghum, Brachypodium, and sugarcane. A translation tool enables cross referencing of Gene IDs between versions of the maize genome. Protein-DNA interactions (PDIs) have been added incorporating results derived from various gene- and TF-centered PDI discovery tools and visualized through a new web-based interface. A filtering tools permits the selection and visualization of PDIs within a ± 2 kb distance from the transcriptional start site (TSS) of a gene of interest. A new BLAST (Basic Local Alignment Search Tool) tool facilitates searching of the maize TFome as well as v3, v4, and v5 TF gene model sets. Lastly, we describe the methodology used to implement GRASSIUS 2.0 which can guide others in developing and updating similar plant gene regulatory knowledgebases.

Open source platform for Estonian speech transcription

AbstractThis paper presents our progress in developing and maintaining a public speech and speaker recognition platform for the Estonian language. The platform consists of a speech processing pipeline and a web-based user interface for end-users, offering transcript post-editing functionality. It is offered for free as a public service and is in active use. The service provides significantly higher speech recognition accuracy than commercial alternatives. We discuss the switch to a workflow management system and how it has improved the core speech processing pipeline. The core systems behind the platform have been made available as open-source code and deployed internally by multiple public and private institutions.

From greatest simplicity to full power

AbstractWhile language processing services are key assets for the science and technology of language, the possible ways under which they may be made available to the widest range of their end users are critical to support an Open Science policy for this scientific domain. Although providing such processing services under some web-based interface, at large, offers itself as an immediate and cogent response to that challenge, turning this view into an effective access to language processing services is an undertaking deserving a clear conceptual direction and a corresponding robust empirical validation. Based on an extensive overview of major undertakings towards making language processing tools available and on the design principles worked out and implemented in the PORTULAN CLARIN infrastructure, in this paper we advocate for a Research-Infrastructure-as-a-Service (RIaaS) model. This model unleashes accessibility to language processing services in as many web-based interface modalities as the current stage of technological development permits to support, in order to serve as many types of end users as possible, from IT developers to Digital Humanities researchers, and including citizen scientists, teachers, students and digital artists among many others.

HERALD: A domain-specific query language for longitudinal health data analytics

BackgroundLarge-scale health data has significant potential for research and innovation, especially with longitudinal data offering insights into prevention, disease progression, and treatment effects. Yet, analyzing this data type is complex, as data points are repeatedly documented along the timeline. As a consequence, extracting cross-sectional tabular data suitable for statistical analysis and machine learning can be challenging for medical researchers and data scientists alike, with existing tools lacking balance between ease-of-use and comprehensiveness. ObjectiveThis paper introduces HERALD, a novel domain-specific query language designed to support the transformation of longitudinal health data into cross-sectional tables. We describe the basic concepts, the query syntax, a graphical user interface for constructing and executing HERALD queries, as well as an integration into Informatics for Integrating Biology and the Bedside (i2b2). MethodsThe syntax of HERALD mimics natural language and supports different query types for selection, aggregation, analysis of relationships, and searching for data points based on filter expressions and temporal constraints. Using a hierarchical concept model, queries are executed individually for the data of each patient, while constructing tabular output. HERALD is closed, meaning that queries process data points and generate data points. Queries can refer to data points that have been produced by previous queries, providing a simple, but powerful nesting mechanism. ResultsThe open-source implementation consists of a HERALD query parser, an execution engine, as well as a web-based user interface for query construction and statistical analysis. The implementation can be deployed as a standalone component and integrated into self-service data analytics environments like i2b2 as a plugin. HERALD can be valuable tool for data scientists and machine learning experts, as it simplifies the process of transforming longitudinal health data into tables and data matrices. ConclusionThe construction of cross-sectional tables from longitudinal data can be supported through dedicated query languages that strike a reasonable balance between language complexity and transformation capabilities.

Accuracy of a Commercial Large Language Model (ChatGPT) to Perform Disaster Triage of Simulated Patients Using the Simple Triage and Rapid Treatment (START) Protocol: Gage Repeatability and Reproducibility Study.

The release of ChatGPT (OpenAI) in November 2022 drastically reduced the barrier to using artificial intelligence by allowing a simple web-based text interface to a large language model (LLM). One use case where ChatGPT could be useful is in triaging patients at the site of a disaster using the Simple Triage and Rapid Treatment (START) protocol. However, LLMs experience several common errors including hallucinations (also called confabulations) and prompt dependency. This study addresses the research problem: "Can ChatGPT adequately triage simulated disaster patients using the START protocol?" by measuring three outcomes: repeatability, reproducibility, and accuracy. Nine prompts were developed by 5 disaster medicine physicians. A Python script queried ChatGPT Version 4 for each prompt combined with 391 validated simulated patient vignettes. Ten repetitions of each combination were performed for a total of 35,190 simulated triages. A reference standard START triage code for each simulated case was assigned by 2 disaster medicine specialists (JMF and MV), with a third specialist (LC) added if the first two did not agree. Results were evaluated using a gage repeatability and reproducibility study (gage R and R). Repeatability was defined as variation due to repeated use of the same prompt. Reproducibility was defined as variation due to the use of different prompts on the same patient vignette. Accuracy was defined as agreement with the reference standard. Although 35,102 (99.7%) queries returned a valid START score, there was considerable variability. Repeatability (use of the same prompt repeatedly) was 14% of the overall variation. Reproducibility (use of different prompts) was 4.1% of the overall variation. The accuracy of ChatGPT for START was 63.9% with a 32.9% overtriage rate and a 3.1% undertriage rate. Accuracy varied by prompt with a maximum of 71.8% and a minimum of 46.7%. This study indicates that ChatGPT version 4 is insufficient to triage simulated disaster patients via the START protocol. It demonstrated suboptimal repeatability and reproducibility. The overall accuracy of triage was only 63.9%. Health care professionals are advised to exercise caution while using commercial LLMs for vital medical determinations, given that these tools may commonly produce inaccurate data, colloquially referred to as hallucinations or confabulations. Artificial intelligence-guided tools should undergo rigorous statistical evaluation-using methods such as gage R and R-before implementation into clinical settings.

Performance of OpenAI’s GPT-4 in a mock MRCS Part A Examination

Introduction: OpenAI’s latest iteration of a Large Language Model (LLM); GPT-4 (Generative Pre-trained Transformer 4) has demonstrated its proficiency against various professional examination standards like the USMLE (United States Medical Licensing Examination), FRCS (Fellowship of the Royal Colleges of Surgeons) and the United States Bar. However, GPT-4’s capability with the MRCS (Membership of the Royal College of Surgeons) Part A has not yet been investigated. Methodology: A representative MRCS Part A examination that was prepared and provided by “TeachMeSurgery” based on the MRCS Intercollegiate Curriculum was used to assess GPT-4's performance. Each question was processed via the web-based interface of ChatGPT (Chat Generative Pre-trained Transformer) Plus. Results: GPT-4 scored 87.2% on Applied Basic Sciences (157/180) and 86.7% on Principles of Surgery in General (104/120 questions), achieving an overall score of 261/300 (87%), which is above the typical passing threshold. GPT-4 scored 100% in four out of the eleven predefined curriculum areas, which included: Pharmacology, Microbiology, Data Interpretation and Audit, and The Surgical Care of Children. GPT-4’s weakest performance was in the Medico-Legal Aspects of Surgical Practice, in which it scored 33.3%. Conclusion: GPT-4 successfully passed the mock MRCS Part A without any specialised preparatory training, however further research could look at integrating the GPT-4 model to enhance a trainee surgeon’s learning and use it as an effective tool to deliver high quality & efficient patient care.

PRIME: A CyberGIS Platform for Resilience Inference Measurement and Enhancement

In an era of increased climatic disasters, there is an urgent need to develop reliable frameworks and tools for evaluating and improving community resilience to climatic hazards at multiple geographical and temporal scales. Defining and quantifying resilience in the social domain is relatively subjective due to the intricate interplay of socioeconomic factors with disaster resilience. To broaden upon it, the choice of indicators and their subsequent ranking for the aggregation into an index is subjective in nature. This aggregation is not empirically validated and is prone to omit the nuances of localized resilience changes and causal factors affecting it, while leading to oversimplified conclusions. Meanwhile, there is a lack of scientifically and computationally rigorous, user-friendly tools that can support customized resilience assessment with consideration of local conditions. This study addresses these gaps through the power of CyberGIS with three objectives: 1) To develop an empirically validated disaster resilience model - Customizable Resilience Inference Measurement (RIM), designed for multi-scale community resilience assessment and influential socioeconomic factors identification; 2) To implement a Platform for Resilience Inference Measurement and Enhancement (PRIME) module in the CyberGISX platform backed by high-performance computing, enabling users to apply and customize RIM to compute and visualize disaster resilience; 3) To demonstrate the utility of PRIME through a representative study to understand the geographical disparities of county-level community resilience to natural hazards in the United States and identifying the driving factors of resilience in the social domain. Customizable RIM generates vulnerability, adaptability, and overall resilience scores derived from empirical parameters—hazard threat, damage, and recovery. Computationally intensive Machine Learning (ML) methods are employed to explain the intricate relationships between these scores and socioeconomic driving factors. PRIME provides a web-based notebook interface guiding users to select study areas, configure parameters, calculate and geo-visualize resilience scores, and interpret socioeconomic factors shaping resilience capacities. A representative study showcases the efficiency of the platform while explaining how the visual results obtained may be interpreted. The essence of this work lies in its comprehensive architecture that encapsulates the requisite data, analytical and geo-visualization functions, and ML models for resilience assessment. This setup provides a foundation for assessing resilience and strategizing enhancement interventions.

Evaluating a Natural Language Processing-Driven, AI-Assisted International Classification of Diseases, 10th Revision, Clinical Modification, Coding System for Diagnosis Related Groups in a Real Hospital Environment: Algorithm Development and Validation Study.

International Classification of Diseases codes are widely used to describe diagnosis information, but manual coding relies heavily on human interpretation, which can be expensive, time consuming, and prone to errors. With the transition from the International Classification of Diseases, Ninth Revision, to the International Classification of Diseases, Tenth Revision (ICD-10), the coding process has become more complex, highlighting the need for automated approaches to enhance coding efficiency and accuracy. Inaccurate coding can result in substantial financial losses for hospitals, and a precise assessment of outcomes generated by a natural language processing (NLP)-driven autocoding system thus assumes a critical role in safeguarding the accuracy of the Taiwan diagnosis related groups (Tw-DRGs). This study aims to evaluate the feasibility of applying an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), autocoding system that can automatically determine diagnoses and codes based on free-text discharge summaries to facilitate the assessment of Tw-DRGs, specifically principal diagnosis and major diagnostic categories (MDCs). By using the patient discharge summaries from Kaohsiung Medical University Chung-Ho Memorial Hospital (KMUCHH) from April 2019 to December 2020 as a reference data set we developed artificial intelligence (AI)-assisted ICD-10-CM coding systems based on deep learning models. We constructed a web-based user interface for the AI-assisted coding system and deployed the system to the workflow of the certified coding specialists (CCSs) of KMUCHH. The data used for the assessment of Tw-DRGs were manually curated by a CCS with the principal diagnosis and MDC was determined from discharge summaries collected at KMUCHH from February 2023 to April 2023. Both the reference data set and real hospital data were used to assess performance in determining ICD-10-CM coding, principal diagnosis, and MDC for Tw-DRGs. Among all methods, the GPT-2 (OpenAI)-based model achieved the highest F1-score, 0.667 (F1-score 0.851 for the top 50 codes), on the KMUCHH test set and a slightly lower F1-score, 0.621, in real hospital data. Cohen κ evaluation for the agreement of MDC between the models and the CCS revealed that the overall average κ value for GPT-2 (κ=0.714) was approximately 12.2 percentage points higher than that of the hierarchy attention network (κ=0.592). GPT-2 demonstrated superior agreement with the CCS across 6 categories of MDC, with an average κ value of approximately 0.869 (SD 0.033), underscoring the effectiveness of the developed AI-assisted coding system in supporting the work of CCSs. An NLP-driven AI-assisted coding system can assist CCSs in ICD-10-CM coding by offering coding references via a user interface, demonstrating the potential to reduce the manual workload and expedite Tw-DRG assessment. Consistency in performance affirmed the effectiveness of the system in supporting CCSs in ICD-10-CM coding and the judgment of Tw-DRGs.

DendroTweaks: An interactive approach for unraveling dendritic dynamics.

Neurons rely on the interplay between dendritic morphology and ion channels to transform synaptic inputs into a sequence of somatic spikes. Detailed biophysical models with active dendrites have been instrumental in exploring this interaction. However, such models can be challenging to understand and validate due to the large number of parameters involved. In this work, we introduce DendroTweaks - a toolbox designed to illuminate how morpho-electric properties map to dendritic events and how these dendritic events shape neuronal output. DendroTweaks features a web-based graphical interface, where users can explore single-cell neuronal models and adjust their morphological and biophysical parameters with real-time visual feedback. In particular, DendroTweaks is tailored to interactive fine-tuning of subcellular properties, such as kinetics and distributions of ion channels, as well as the dynamics and allocation of synaptic inputs. It offers an automated approach for standardization and refinement of voltage-gated ion channel models to make them more comprehensible and reusable. The toolbox allows users to run various experimental protocols and record data from multiple dendritic and somatic locations, thereby enhancing model validation. Finally, it aims to deepen our understanding of which dendritic properties are essential for neuronal input-output transformation. Using this knowledge, one can simplify models through a built-in morphology reduction algorithm and export them for further use in faster, more interpretable networks. With DendroTweaks, users can gain better control and understanding of their models, advancing research on dendritic input-output transformations and their role in network computations.

DTVF: A User-Friendly Tool for Virulence Factor Prediction Based on ProtT5 and Deep Transfer Learning Models.

Virulencefactors (VFs) are key molecules that enable pathogens to evade the immune systems of the host. These factors are crucial for revealing the pathogenic processes of microbes and drug discovery. Identification of virulence factors in microbes become an important problem in the field of bioinformatics. To address this problem, this study proposes a novel model DTVF (Deep Transfer Learning for Virulence Factor Prediction), which integrates the ProtT5 protein sequence extraction model with a dual-channel deep learning model. In the dual-channel deep learning model, we innovatively integrate long short-term memory (LSTM) with convolutional neural networks (CNNs), creating a novel integrated architecture. Furthermore, by incorporating the attention mechanism, the accuracy of VF detection was significantly enhanced. We evaluated the DTVF model against other excellent-performing models in the field. DTVF demonstrates superior performance, achieving an accuracy rate of 84.55% and an AUROC of 92.08% on the benchmark dataset. DTVF shows state-of-the-art performance in this field, surpassing the existing models in nearly all metrics. To facilitate the use of biologists, we have also developed an interactive web-based user interface version of DTVF based on Gradio.

Robot Vehicle with ESP32 CAM

Abstract: A surveillance robot equipped with ESP32-CAM is a setup that assembles a portable surveillance gadget from a robot frame and the ESP32-CAM board. The ESP32-CAM board is an affordable development platform featuring an integrated camera and Wi-Fi capabilities. This robot is capable of navigating and capturing video footage across various settings due to its frame. The gadget can be operated through a web-based interface on the ESP32-CAM board. Through this interface, users can direct the robot's movements, view real-time video feeds, and capture video snapshots. Moreover, the system can be configured to alert the user whenever it detects movement through computer vision methods such as tracking and identifying objects. This ESP32-CAM surveillance robot finds applications in home security, monitoring from afar, and industrial surveillance. Its low cost and user-friendly interface make it an attractive choice for those looking to remotely oversee their surroundings.

Continuous monitoring of coal mine dangers with an automated internet of things system

The safety of mine workers is a major concern in the modern world. The life and health of miners are vulnerable against a few fundamental problems, such as their workplace and its adverse effects. A novel and inventive approach is needed to increase profitability and reduce mining costs while keeping worker safety in mind. For tracking the level of concentration of hazardous gases, semiconductor gas sensors are used. In the mine worker area, air contamination is primarily caused by outflows from particulate matter and gases, such as sulfur dioxide (SO 2), nitrogen dioxide (NO 2), carbon monoxide (CO) furthermore, the goal of this project is to design a real time IOT system that can monitor temperature, humidity, dangerous gasses, and smoke status in an underground mine utilizing sensors with an ODROID-N2+controller and designed a base station to receive data from all coal mines via the ODROID-N2+module. We also built a web-based interface accessible through computers and Android/iOS devices. The suggested system seamlessly integrates surveillance, analysis, and localization strategies using cloud computing, application gateways, real-time operational databases, Internet of Things (IoT), and application program interfaces to enhance the management of safety and prevent injuries in underground coal mines.

IPhyDSDB: Phytoplasma Disease and Symptom Database.

Phytoplasmas are small, intracellular bacteria that infect a vast range of plant species, causing significant economic losses and impacting agriculture and farmers' livelihoods. Early and rapid diagnosis of phytoplasma infections is crucial for preventing the spread of these diseases, particularly through early symptom recognition in the field by farmers and growers. A symptom database for phytoplasma infections can assist in recognizing the symptoms and enhance early detection and management. In this study, nearly 35,000 phytoplasma sequence entries were retrieved from the NCBI nucleotide database using the keyword "phytoplasma" and information on phytoplasma disease-associated plant hosts and symptoms was gathered. A total of 945 plant species were identified to be associated with phytoplasma infections. Subsequently, links to symptomatic images of these known susceptible plant species were manually curated, and the Phytoplasma Disease Symptom Database (iPhyDSDB) was established and implemented on a web-based interface using the MySQL Server and PHP programming language. One of the key features of iPhyDSDB is the curated collection of links to symptomatic images representing various phytoplasma-infected plant species, allowing users to easily access the original source of the collected images and detailed disease information. Furthermore, images and descriptive definitions of typical symptoms induced by phytoplasmas were included in iPhyDSDB. The newly developed database and web interface, equipped with advanced search functionality, will help farmers, growers, researchers, and educators to efficiently query the database based on specific categories such as plant host and symptom type. This resource will aid the users in comparing, identifying, and diagnosing phytoplasma-related diseases, enhancing the understanding and management of these infections.

Enhancing GWOPS Capabilities for Coordinated Multi-Telescope Detection of Gravitational Wave Electromagnetic Counterparts

The groundbreaking detection of gravitational waves (GWs) has ushered in a new era of astronomical observation, granting us access to cosmic phenomena that are imperceptible to electromagnetic waves. The inherently weak GW signals coupled with the substantial uncertainties in source localization pose significant challenges to the field of astronomy. In this paper, we introduce innovative strategies to enhance the efficiency of observing electromagnetic counterparts to GW events, thereby unlocking further secrets of the cosmos. We present a novel technique for designing observation targets and establishing priorities, progressing from the epicenter to the periphery within the boundaries of the GW error sky region. This method has significantly reduced the average slewing distance of telescopes by 41% compared to traditional methods, thus enhancing observational efficiency. Additionally, we have developed a collaborative observation strategy for telescope networks, allocating observation targets based on the field-of-view (FOV) sizes of individual telescopes. This ensures comprehensive coverage without redundancy, allowing a network of four telescopes to cover a sky area and accumulate observation probability more than four times that of a single telescope operating independently over an equivalent period. Building upon these strategies, we have significantly upgraded GWOPS, the GW Follow-up Observation Planning System developed by the China-VO team, to provide precise observational planning for large FOV (greater than 1 square degree) telescope networks. The system also features a web-based user interface that presents the GW error sky area and observation planning results in a graphical format, significantly improving user interaction and experience. The research presented herein equips astronomers with a robust toolkit, advancing the efficiency of searching for and studying electromagnetic counterparts to GW events, and heralding new frontiers in the research of astrophysics and cosmology.