Multi-platform Systems Research Articles

Advances in Plant Disease Diagnostics and Surveillance- A review

Plant diseases pose a significant threat to global food security, leading to substantial yield losses and economic impacts. Early detection and effective monitoring are crucial for managing plant diseases, yet traditional diagnostic methods such as visual inspections, serological tests, and molecular assays face limitations in sensitivity, specificity, and scalability. In recent years, advancements in diagnostic and surveillance technologies have revolutionized plant health management. Next-Generation Sequencing (NGS) enables comprehensive pathogen profiling, while CRISPR-based diagnostics offer rapid and highly specific detection. Similarly, biosensors and portable devices provide on-site diagnostics, and machine learning and AI applications enhance the analysis of complex datasets, supporting automated disease identification and predictive modeling. Concurrently, advances in disease surveillance through remote sensing technologies, including satellites and Unmanned Aerial Vehicles (UAVs), enable large-scale, real-time monitoring of crop health, detecting disease outbreaks and facilitating targeted interventions. Integrating these diverse technologies into multi-platform systems offers a holistic approach to plant disease management, combining molecular diagnostics, environmental monitoring, and digital platforms to support data-driven decision-making. Several challenges remain, including high costs, technical complexities, and the need for standardized data integration. Addressing these barriers is essential to ensure that these technologies are accessible and effective across various agricultural systems, particularly in resource-limited settings. Future research should focus on enhancing the robustness, affordability, and scalability of these tools while promoting interdisciplinary collaborations.

Factorial design applied to LC-ESI-QTOF mass spectrometer parameters for untargeted metabolomics.

Investigations of untargeted metabolomics are based on high-quality data acquisition usually from multiplatform systems that include high-resolution mass spectrometry equipment. The comprehensive set of results is used as data entry for bioinformatics and machine learning sciences to access reliable metabolic and biochemical information for clinical, forensic, environmental, and endless applications. In this context, design of experiments is a powerful tool for optimizing data acquisition procedures, using a multivariate approach, which enables the maximization of a high-quality amount of information with reduced number of tests. In this study, we applied a 33 Box-Behnken factorial design with central point triplicate for optimizing the ionization of an HPLC-ESI-QTOF method used for screening urine samples. Nozzle voltage (V), fragmentor voltage (V) and nebulizer pressure (psig) were the factors selected for variation. The response surface methodology was applied in the molecular features extracted at each level, resulting in a statistical model that helps evaluating the synergic interaction between these factors. Together with the qualitative analysis of the resulting total ion chromatograms, we came across a reproducible (6.14% RSD) and highly efficient method for untargeted metabolomics of human urine samples. The proposed method can be useful for applications in several urine-based metabolomics-driven studies, as the factorial design can be applied in the development of any analytical protocol considering different LC-MS setups.

DCNNet: A Distributed Convolutional Neural Network for Remote Sensing Image Classification

With the development of information technology, multi-platform collaborative collection and processing of remote sensing images has become a significant trend. However, the existing models are challenging to achieve accurate and efficient image interpretation on remote sensing multi-platform systems. To solve this problem, we propose a novel distributed convolutional neural network (DCNNet) and demonstrate the superiority of our method in remote sensing image classification. Firstly, a progressive inference mechanism is introduced to support most images to be classified in advance with satisfactory accuracy, which minimises redundant cloud transmission and achieves higher inference acceleration. Meanwhile, a distributed self-distillation paradigm is designed to integrate and refine in-depth features, performing efficient knowledge transfer between terminals and the cloud network. Secondly, a multi-scale feature fusion (MSFF) module is presented to extract valid receptive fields and assign weights to crucial channel dimension features. Finally, a sampling augmentation (SA) attention is proposed to enhance the effective feature representation of RS images through a bottom-up and top-down feedforward structure. We conducted extensive experiments and visual analyses on three benchmark scene classification datasets and one fine-grained dataset. Compared with the existing methods, DCNNet consolidates several advantages in terms of accuracy, computation, transmission and processing efficiency into a single framework for multi-platform remote sensing image classification.

Acceptance of Electronic Health Records in Traditional Malay Medicine: An Exploratory Prototyping Approach

The usage of electronic health records is widely used today in the modern medicine field due to its immense benefits. These benefits include and are not limited to allowing physician to access a patient’s chart remotely, provide alerts to a potential medication error and critical lab values. However, the usage of this technology in Traditional Malay Medicine is limited. This paper explores the acceptance usage of electronic health records in Traditional Malay Medicine in five healthcare organisations including private Traditional Malay Medicine practitioners in Malaysia. This research utilizes the exploratory prototyping approach where a prototype, MyPostnatalSys is developed and used to gain constructive feedback of the acceptance of electronic health records. MyPostnatalSys, an electronic health record prototype, was developed for integrating Traditional Malay Medicine and Modern Medicine services using an interoperability standard, HL7. The purpose of interoperability standards is to ensure patient data can be transmitted across multi-platform systems seamlessly. The results show that more than 90% respondents have shown a positive acceptance on the usage of electronic health records in Traditional Malay Medicine. However, there may exist some resistance in accepting electronic health records technology in private Traditional Malay Medicine practitioners. However Traditional Malay practitioners trained by a government led initiative for women’s health, Mamacare programme, are more open to accept the implementation. This shows that upskilling programmes such as Mamacare can reduce the barriers of acceptance in electronic health records in Traditional Malay practitioners.

Magnetic Resonance Spectroscopy: Information and Efficiency of Diagnostics of Malignant Tumors (Review and Portfolio)

The potential field of magnetic resonance spectroscopy (MRS) application in the clinical practice of oncology is trivial: differential diagnosis of tumors, analysis of tumor malignancy degree, visualization of tumor response to chemotherapy, radiation therapy, and prediction of treatment outcome. The purpose of the work is to present, within the framework of infographics, the initial visual data of MRS of malignant tumors of various localizations for promising multiplatform systems of scientific visualization. Recall that scientific visualization deals with issues of accurate and understandable display of objects, processes and phenomena of scientific/practical interest. In the aspect of infographics, representative spectra of normal and tumor of brain tissues, auditory nerve, thyroid gland, lungs, mammary gland, pancreas, liver, kidneys, ovaries, uterus, prostate gland, muscles, and bones are presented. The main defined 1H-MPC tumor metabolites are described. Many tumors are overlapped in spectrum, and MRI results are best interpreted in conjunction with other imaging and histological analysis findings. The potential of MRS technologies can be fully realized only in superposition with artificial intellect.

Towards the Interoperability of IoT Platforms: A Case Study for Data Collection and Data Storage

Abstract Interoperability is one of the biggest challenges in IoT. There is a need to connect tools and services from different platforms to improve performance and flexibility as well as to get rid of vendor-locks. Moreover, fully established practices to classify IoT systems to support the development of multi-platform systems have not been grounded. This paper addresses these issues on a two-fold approach. Firstly, by introducing a classification that is based on the tasks performed in IoT system. Secondly by proposing a conceptual middleware to connect different IoT platforms as for a possible solution for interoperability issues in IoT.

Research on Economic Evaluation Methods of Offshore Oil Multi-Platform Interconnected Power System Considering Petroleum Production Characteristics

Offshore oil multi-platform interconnected power system is developing rapidly. The proposal of an effective economic evaluation method that fits the actual production situation of offshore oilfields is very meaningful for the planning and construction of multi-platform interconnected power systems. This article proposes the electric depreciation, depletion, and amortization (DD&A) barrel oil cost S and maximum expected benefit per unit power generation Ie as economic indicators, considering the actual production characteristics and life cycle of the oil field. In order to build a complete economic evaluation system, this article also introduces the N−1 pass rate ηN−1, voltage qualification rate γ, power supply reliability ASAI (Average Service Availability Index), and other reliability indicators to evaluate the offshore power system. When calculating the weight of the indicators, analytic hierarchy method (AHP) was applied to calculate subjective weights, and an entropy method was applied to calculate objective weights. To unify the two weights, the ideal point method is proposed to obtain compound weights. Finally, this article selects an offshore oil field in Bohai Bay, China as example, and analyses short-term small-scale, long-term large-scale, and actual power system as calculation examples in different planning periods. The analysis result verifies the effectiveness of the economic evaluation method.

Interplanetary CubeSats for asteroid exploration: Mission analysis and design

Recent advances in CubeSats technology are leading the transition from purely education tools to actual scientific missions. The small volumes and masses, the versatile purpose, as well as the fast development time associated with a potential high return-to-cost ratio are at the origin of the increasing number of new mission proposals, also beyond low Earth orbit (LEO). The purpose of this study is to assess the CubeSats ability to complement an interplanetary scientific mission. The proposed AIDA (Asteroid Impact and Deflection Assessment) mission, an ESA/NASA joint effort to demonstrate the kinetic impact technique to change the motion of an asteroid in space, has been selected as case study, having a mission context particularly suitable in showing CubeSats supporting capabilities. The feasibility study of a mission involving the use of CubeSats as secondaries for technology demonstration and science purposes was performed. Mission objectives and requirements were defined, followed by the development of concepts of operations and mission architectures proposals. Eventually, multiple trade-off tools were adopted to define the proposed mission baseline, which involves the deployment of two 3U CubeSats performing a detachment to achieve the final configuration of one 2U and four 1U CubeSats. The scientific campaign conducted by the CubeSats includes gravitational and magnetic field mapping, on-surface chemical-physical measurements via multiple wide chip-size-sensor nets deployed from orbit, on-surface seismic measurements via landing of the 2U CubeSat, direct observation of the impact from multiple viewpoints and evaluation of the asteroid's orbit deflection due to the impact. The mission proposed involves also some important technological demonstrators. The S-iEPS (Scalable-ion Electrospray Propulsion System) has been considered as propulsion system, while a potential landing system has been proposed to achieve the soft touchdown of the 2U CubeSat. Finally, an inter-satellite communication link via laser has been included as main communication system. The proposed mission baseline has shown that CubeSats can be successfully integrated as multi-platform systems to provide useful support to interplanetary missions. This solution may enable the capability to acquire more detailed information with the possibility to combine them to obtain better results with respect to single-platform systems. The proposed mission concept represents a valuable low-cost piggyback solution adaptable to several mission contexts, with a potential high return and a remarkable attitude for the implementation and testing of new technologies and operations. In this context, this study provides a useful framework for the design and development of interplanetary CubeSats missions.

A unified approach to adapt scientific visualization systems to third-party solvers

This paper presents a new unified approach to adapt scientific visualization systems to third-party solvers implemented on different software and hardware platforms. This approach allows building multiplatform visualization systems, enables automatic conversion of input and output data from any solver into a rendering-compatible format, and provides real-time generation of high-quality images. The automated adaptation of visualization systems to third-party solvers is based on ontological engineering methods. Multiplatform portability is provided by the automatic generation of a graphical user interface (GUI) for each particular operating system and by preprocessing the data to be rendered by using heuristic-based tools, which ensures compatibility with different hardware and software platforms, including desktop computers and mobile devices. In addition, an original anti-aliasing algorithm is proposed to ensure high quality of resulting images. Based on the proposed approach, a multiplatform scientific visualization system called SciVi is developed, which is successfully used for solving various real-world scientific visualization problems from different application domains.

QUALITY ANALYSIS OF 3D SURFACE RECONSTRUCTION USING MULTI-PLATFORM PHOTOGRAMMETRIC SYSTEMS

In recent years, the necessity of accurate 3D surface reconstruction has been more pronounced for a wide range of mapping, modelling, and monitoring applications. The 3D data for satisfying the needs of these applications can be collected using different digital imaging systems. Among them, photogrammetric systems have recently received considerable attention due to significant improvements in digital imaging sensors, emergence of new mapping platforms, and development of innovative data processing techniques. To date, a variety of techniques haven been proposed for 3D surface reconstruction using imagery collected by multi-platform photogrammetric systems. However, these approaches suffer from the lack of a well-established quality control procedure which evaluates the quality of reconstructed 3D surfaces independent of the utilized reconstruction technique. Hence, this paper aims to introduce a new quality assessment platform for the evaluation of the 3D surface reconstruction using photogrammetric data. This quality control procedure is performed while considering the quality of input data, processing procedures, and photo-realistic 3D surface modelling. The feasibility of the proposed quality control procedure is finally verified by quality assessment of the 3D surface reconstruction using images from different photogrammetric systems.

High-throughput, multi-platform metabolomics on very small volumes: 1H NMR metabolite identification in an unadulterated tube-in-tube system

As small animal models of disease become more widely used, there is increasing importance and potential for characterizing their metabolomes. However, as the animal becomes smaller, the amounts of biofluids such as urine and cerebral spinal fluid available for metabolomic studies are more limited. Further, in multi-platform systems biology when the same small sample must be used for several analyses, it is a frequent requirement that no additions are made to the sample (even as simple as D2O or an NMR chemical shift reference) to maintain sample integrity. Herein we describe a method for high-throughput 1H-NMR studies using ~30 µl volumes, suitable for biofluid matrices. The compartmentalization of the sample and NMR standards, however, requires chemical shift corrections due to bulk magnetic susceptibility and ionic strength changes for metabolite profiling using a reference library or data-binning of the chemical shift axis. This set-up minimizes the cost of individual data collection per small animal and is suitable for high-throughput, longitudinal, multimodal metabolomic studies of biofluids available in limited quantities.

Electronic sketching on a multi-platform context: A pilot study with developers

During the past 45 years there has been a recurrence of interest on supporting sketching at electronic devices and interactive surfaces, and despite being sketching recognition fairly well addressed on the literature, the adoption of electronic sketching as a design tool is still a challenge.The current popularization of touch screen devices allows designers to sketch using their device of preference, while the current multi-platform capabilities made possible by HTML5 allows sketching systems to run on many devices at the same time. Those two factors combined might pose new opportunities for researchers to explore how designers use sketching on flexible setups by combining heterogeneous sketching devices for design sessions.This may arise new possibilities in the field of prototyping user interfaces since, by using such multi-platform systems, designers would now be able of designing interfaces for multiple devices by producing and testing them on the device itself.This paper reports a pilot experiment conducted with 6 developers, grouped into pairs on design sessions using Gambit – a multi-platform sketching system that provides a lightweight approach for prototyping user interfaces for many devices at once. We performed a discourse analysis of the professionals based on recorded videos of interviews conducted during and after design sessions with the system and aggregated the data in order to investigate the main requirements for multi-platform sketching systems.

Architecture and Methods for Innovative Heterogeneous Wireless Sensor Network Applications

Nowadays wireless sensor netwoks (WSN) technology, wireless communications and digital electronics have made it realistic to produce a large scale miniaturized devices integrating sensing, processing and communication capabilities. The focus of this paper is to present an innovative mobile platform for heterogeneous sensor networks, combined with adaptive methods to optimize the communication architecture for novel potential applications in multimedia and entertainment. In fact, in the near future, some of the applications foreseen for WSNs will employ multi-platform systems with a high number of different devices, which may be completely different in nature, size, computational and energy capabilities, etc. Nowadays, in addition, data collection could be performed by UAV platforms which can be a sink for ground sensors layer, acting essentially as a mobile gateway. In order to maximize the system performances and the network lifespan, the authors propose a recently developed hybrid technique based on evolutionary algorithms. The goal of this procedure is to optimize the communication energy consumption in WSN by selecting the optimal multi-hop routing schemes, with a suitable hybridization of different routing criteria. The proposed approach can be potentially extended and applied to ongoing research projects focused on UAV-based sensing with WSN augmentation and real-time processing for immersive media experiences.

A Web-accessible distributed data warehouse for brain tumour diagnosis

AbstractCurrently, biological databases (DBs) are a common tool to complement the research of a wide range of biomedical disciplines, but there are only a few specialized medical DBs for human brain tumour magnetic resonance spectroscopy (MRS) data; they typically store a limited range of biological data (i.e. clinical information, magnetic resonance imaging and MRS data) and are not offered as open-source Structured Query Language relational DB schemas. We present a novel approach to biological DBs: a distributed Web-accessible DB for storing and managing clinical and biomedical data related to brain tumours from different clinical centres. This tool is designed for multi-platform systems with dissimilar DB management systems. Being the main data repository of the HealthAgents (HA) project, it uses multi-agent technology and allows the centres to share data and obtain diagnosis classifications from other centres distributed around the world in a reliable way.The HA project aims to create an agent-based distributed decision support system (DSS) to assist doctors to provide a brain tumour diagnosis and prognosis. The HA DB enables the DSS to totally integrate with its Graphical User Interface to perform classifications with the stored data and visualize the results using the HA distributed agents framework. This new feature converts the system presented in the first application in the world to combine a storage and management tool for brain tumour data and a complete Web-based DSS to obtain automatic diagnosis.

Pre-DFT Processing for MIMO-OFDM Systems with Space-Time-Frequency Coding

Subcarrier based space processing was conventionally employed in Orthogonal Frequency Division Multiplexing (OFDM) systems under Multiple-Input and Multiple-Output (MIMO) channels to achieve optimal performance. At the receiver of such systems, multiple Discrete Fourier Transform (DFT) blocks, each corresponding to one receive antenna, are required to be used. This induces considerable complexity. In this paper, we propose a pre-DFT processing scheme for the receiver of MIMO-OFDM systems with space-time-frequency coding. With the proposed scheme, the number of DFT blocks at the receiver can be any number from one to the number of receive antennas, thus enabling effective complexity and performance tradeoff. Using the pre-DFT processing scheme, the number of input signals to the space-time-frequency decoder can be reduced compared with the subcarrier based space processing. Therefore, a high dimensional MIMO system can be shrunk into an equivalently low dimension one. Due to the dimension reduction, both the complexity of the decoder and the complexity of channel estimation can be reduced. In general, the weighting coefficients calculation for the pre-DFT processing scheme should be relevant to the specific space-time-frequency code employed. In this paper, we propose a simple universal weighting coefficients calculation algorithm that can be used to achieve excellent performance for most practical space-time-frequency coding schemes. This makes the design of the pre-DFT processing scheme independent of the optimization of the space-time-frequency coding, which is desirable for multiplatform systems.