Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals is important to design targeted prevention strategies. In this paper, we present an automatic tool that uses machine learning techniques to predict the development of type 2 diabetes mellitus (T2DM). Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). We trained and validated the models using the OGTT and demographic data of 1,492 healthy individuals collected during the San Antonio Heart Study. This study collected plasma glucose and insulin concentrations before glucose intake and at three time-points thereafter (30, 60 and 120 min). Furthermore, personal information such as age, ethnicity and body-mass index was also a part of the data-set. Using 11 OGTT measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using minimum redundancy maximum relevance feature selection algorithm. All possible combinations of the 10 best ranked features were used to generate SVM based prediction models. This research shows that an individual’s plasma glucose levels, and the information derived therefrom have the strongest predictive performance for the future development of T2DM. Significantly, insulin and demographic features do not provide additional performance improvement for diabetes prediction. The results of this work identify the parsimonious clinical data needed to be collected for an efficient prediction of T2DM. Our approach shows an average accuracy of 96.80% and a sensitivity of 80.09% obtained on a holdout set.

Background: The demand for effective use of water resources has increased because of ongoing global climate transformations in the agriculture science sector. Cost-effective and timely distributions of the appropriate amount of water are vital not only to maintain a healthy status of plants leaves but to drive the productivity of the crops and achieve economic benefits. In this regard, employing a terahertz (THz) technology can be more reliable and progressive technique due to its distinctive features. This paper presents a novel, and non-invasive machine learning (ML) driven approach using terahertz waves with a swissto12 material characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz in real-life digital agriculture interventions, aiming to develop a feasible and viable technique for the precise estimation of water content (WC) in plants leaves for 4 days. For this purpose, using measurements observations data, multi-domain features are extracted from frequency, time, time–frequency domains to incorporate three different machine learning algorithms such as support vector machine (SVM), K-nearest neighbour (KNN) and decision-tree (D-Tree).Results: The results demonstrated SVM outperformed other classifiers using tenfold and leave-one-observations-out cross-validation for different days classification with an overall accuracy of 98.8%, 97.15%, and 96.82% for Coffee, pea shoot, and baby spinach leaves respectively. In addition, using SFS technique, coffee leaf showed a significant improvement of 15%, 11.9%, 6.5% in computational time for SVM, KNN and D-tree. For pea-shoot, 21.28%, 10.01%, and 8.53% of improvement was noticed in operating time for SVM, KNN and D-Tree classifiers, respectively. Lastly, baby spinach leaf exhibited a further improvement of 21.28% in SVM, 10.01% in KNN, and 8.53% in D-tree in overall operating time for classifiers. These improvements in classifiers produced significant advancements in classification accuracy, indicating a more precise quantification of WC in leaves.Conclusion: Thus, the proposed method incorporating ML using terahertz waves can be beneficial for precise estimation of WC in leaves and can provide prolific recommendations and insights for growers to take proactive actions in relations to plants health monitoring.

In this paper, we propose and experimentally demonstrate a novel hybrid chaos-based three-dimensional (3-D) constellation scrambling scheme to simultaneously improve the physical layer security and transmission performance of the coherent optical orthogonal frequency division multiplexing (CO-OFDM) system. A 3-D regular hexahedron signal constellation is constructed by the constellation figure of merit principle, which not only expands the encryption dimension but improves the error performance. The dynamic parameters for constellation scrambling are generated by the 5-D hybrid chaotic scheme based on the combination of a 3-D hyperchaotic Hénon mapping and two independent 1-D Logistic mappings, as such a key space of ∼10 ¹³³ is introduced to enhance the security level of OFDM data encryption during transmission. Furthermore, a transmission experiment for encryption of 144 Gbps 16-quadrature-amplitude-modulation OFDM data over a 100 km standard single-mode fiber in a CO-OFDM system is demonstrated. Compared with the case of using the 3-D rectangular constellation, a 2 dB bit error rate performance improvement is achieved. The results show that the proposed scheme could effectively enhance the system security and transmission performance, which suggests a scalable strategy for future physically secured CO-OFDM systems.

This article presents a novel terahertz (THz) antenna array design comprising a layered structure of a perovskite material which enhances the radiation characteristics of an antenna overlaid on a conventional metallic antenna element. The simulated antenna consists of a THz gold patch antenna coated with a hybrid perovskite material, methyl-ammonium lead iodide CH ₃ NH ₃ PbI ₃ which enables the manipulation of the THz electromagnetic waves. In addition to this, we also present a comparison of the antenna properties of the proposed hybrid perovskite material with antennas made of gold and perovskite only. The proposed antenna operates in the frequency band 0.9 - 1.2 THz. The simulated impedance bandwidth of the proposed array antenna ranges from 0.9 THz to 1.2 THz with a reflection coefficient (S11) less than -10 dB. The antenna array has a radiation patterns stability on the whole frequency band. The peak gain obtained is 11.4 dBi with perovskite arrays. The hybrid and perovskite antenna array demonstrate high radiation efficiency. The designs presented here will help in realising future wireless communication systems that require miniaturisation, fast reconfigurability and wearability.

The microwave frequency band typically used for wireless communications will soon become saturated and will no longer be able to fulfill the high bandwidth demands of modern communication networks. Terahertz (THz) communication has appeared as a highly attractive, future-generation wireless technology that offers higher spectral bandwidth and, therefore, higher data rates. However, the full exploitation of THz technologies is contingent upon the availability of energy-efficient sources and devices. In this letter, we present a fabrication and measurement of a microscale planar inverted cone antenna array made of gold. Using an ungrounded coplanar waveguide (CPW) feed, the microfabricated structure provides a bandwidth of 37.9% with the resonant frequency of 0.925 THz. Given the cost of microfabrication is reduced substantially with rapid technological advancements, the results of this letter suggest that high-speed THz communications can be realized for wide-scale applications.

An increasing global aridification due to climate change has made the health monitoring of vegetation indispensable to maintaining the food supply chain. Cost-effective and smart irrigation systems are required not only to ensure the efficient distribution of water, but also to track the moisture of plant leaves, which is an important marker of the overall health of the plant. This paper presents a novel electromagnetic method to monitor the water content (WC) and characterisation in plant leaves using the absorption spectra of water molecules in the terahertz (THz) frequency for four consecutive days. We extracted the material properties of leaves of eight types of pot herbs from the scattering parameters, measured using a material characterisation kit in the frequency range of 0.75 to 1.1 THz. From the computed permittivity, it is deduced that the leaf specimens increasingly become transparent to the THz waves as they dry out with the passage of days. Moreover, the loss in weight and thickness of leaves were observed due to the natural evaporation of leaf moisture cells and change occurred in the morphology of fresh and water-stressed leaves. It is also illustrated that loss observed in WC on day 1 was in the range of 5% to 22%, and increased from 83.12% to 99.33% on day 4. Furthermore, we observed an exponential decaying trend in the peaks of the real part of the permittivity from day 1 to 4, which was reminiscent of the trend observed in the weight of all leaves. Thus, results in paper demonstrated that timely detection of water stress in leaves can help to take proactive action in relation to plants health monitoring, and for precision agriculture applications, which is of high importance to improve the overall productivity.