Machine Learning Driven Approach Towards the Quality Assessment of Fresh Fruits Using Non-invasive Sensing

In agriculture science, accurate information of moisture content (MC) in fruits and vegetables in an automated fashion can be vital for astute quality and grading evaluation. This demands for a viable, feasible and cost-effective technique for the defect recognition using timely detection of MC in fruits and vegetables to maintain a healthy sensory characteristic of fruits. Here we propose a non-invasive machine learning (ML) driven technique to monitor variations of MC in fruits using the terahertz (THz) waves with Swissto12 material characterization kit (MCK) in the frequency range of 0.75 THz to 1.1 THz. In this regard, multi-domain features are extracted from time-, frequency-, and time-frequency domains, and applied three ML algorithms such as support vector machine (SVM), knearest neighbour (KNN) and Decision Tree (D-Tree) for the precise assessment of MC in both apple and mango slices. The results illustrated that the performance of SVM exceeded other classifiers results using 10-fold validation and leave-oneobservation-out-cross-validation techniques. Moreover, all three classifiers exhibited 100 accuracy for day 1 and 4 with 80% MC value (freshness) and 2% MC value (staleness) of both fruits’ slices, respectively. Similarly, for day 2 and 3, an accuracy of 95% was achieved with intermediate MC values in both fruits’ slices. This study will pave a new direction for the real-time quality evaluation of fruits in a non-invasive manner by incorporating ML with THz sensing at a cellular level. It also has a strong potential to optimize economic benefits by the timely detection of fruits quality in an automated fashion

Reinforcement Learning Driven Energy Efficient Mobile Communication and Applications

Smart city planning is envisaged as advance technology based independent and autonomous environment enabled by optimal utilisation of resources to meet the short and long run needs of its citizens. It is therefore, preeminent area of research to improve the energy consumption as a potential solution in multi-tier 5G Heterogeneous Networks (HetNets). This article predominantly focuses on energy consumption coupled with CO 2 emissions in cellular networks in the context of smart cities. We use Reinforcement Learning (RL) vertical traffic offloading algorithm to optimize energy consumption in Base Stations (BSs) and to reduce carbon footprint by applying widely accepted strategy of cell switching and traffic offloading. The algorithm relies on a macro cell and multiple small cells traffic load information to determine the cell offloading strategy in most energy efficient way while maintaining quality of service demands and fulfilling users applications. Spatio-temporal simulations are performed to determine a cell switch on/off operation and offload strategy using varying traffic conditions in control data separated architecture. The simulation results of the proposed scheme prove to achieve reasonable percentage of energy and CO 2 reduction

High-Throughput Experimentation, Theoretical Modeling, and Human Intuition: Lessons Learned in Metal–Organic-Framework-Supported Catalyst Design

We have screened an array of 23 metals deposited onto the metal-organic framework (MOF) NU-1000 for propyne dimerization to hexadienes. By a first-of-its-kind study utilizing data-driven algorithms and high-throughput experimentation (HTE) in MOF catalysis, yields on Cu-deposited NU-1000 were improved from 0.4 to 24.4%. Characterization of the best-performing catalysts reveal conversion to hexadiene to be due to the formation of large Cu nanoparticles, which is further supported by reaction mechanisms calculated with density functional theory (DFT). Our results demonstrate both the strengths and weaknesses of the HTE approach. As a strength, HTE excels at being able to find interesting and novel catalytic activity; any a priori theoretical approach would be hard-pressed to find success, as high-performing catalysts required highly specific operating conditions difficult to model theoretically, and initial simple single-atom models of the active site did not prove representative of the nanoparticle catalysts responsible for conversion to hexadiene. As a weakness, our results show how the HTE approach must be designed and monitored carefully to find success; in our initial campaign, only minor catalytic performances (up to 4.2% yield) were achieved, which were only improved following a complete overhaul of our HTE approach and questioning our initial assumptions

Electrophilic Organoiridiunn(III) Pincer Complexes on Sulfated Zirconia for Hydrocarbon Activation and Functionalization

Single-site supported organometallic catalysts bring together the favorable aspects of homogeneous and heterogeneous catalysis while offering opportunities to investigate the impact of metal–support interactions on reactivity. We report a (dmPhebox)Ir(III) (dmPhebox = 2,6-bis(4,4-dimethyloxazolinyl)-3,5-dimethylphenyl) complex chemisorbed on sulfated zirconia, the molecular precursor for which was previously applied to hydrocarbon functionalization. Spectroscopic methods such as diffuse reflectance infrared Fourier transformation spectroscopy (DRIFTS), dynamic nuclear polarization (DNP)-enhanced solid-state nuclear magnetic resonance (SSNMR) spectroscopy, and X-ray absorption spectroscopy (XAS) were used to characterize the supported species. Tetrabutylammonium acetate was found to remove the organometallic species from the surface, enabling solution-phase analytical techniques in conjunction with traditional surface methods. Cationic character was imparted to the iridium center by its grafting onto sulfated zirconia, imbuing high levels of activity in electrophilic C–H bond functionalization reactions such as the stoichiometric dehydrogenation of alkanes, with density functional theory (DFT) calculations showing a lower barrier for β-H elimination. Catalytic hydrogenation of olefins was also facilitated by the sulfated zirconia-supported (dmPhebox)Ir(III) complex, while the homologous complex on silica was inactive under comparable conditions

Wireless on walls: revolutionizing the future of health care

No abstract available

Machine learning driven non-invasive approach of water content estimation in living plant leaves using terahertz waves

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

Ammonia Capture within Isoreticular Metal-Organic Frameworks with Rod Secondary Building Units

The efficient removal, capture, and recycling of ammonia (NH3) constitutes a demanding process, thus the development of competent adsorbent materials is highly desirable. The implementation of metal-organic frameworks (MOFs), known for their tunability and high porosity, has attracted much attention for NH3 adsorption studies. Here, we report three isoreticular porphyrin-based MOFs containing aluminum (Al-PMOF), gallium (Ga-PMOF), and indium (In-PMOF) rod secondary building units with Brønsted acidic bridging hydroxyl groups. NH3 sorption isotherms in Al-PMOF demonstrated reversibility in isotherms. In contrast, the slopes of the adsorption isotherms in Ga-PMOF and In-PMOF were much steeper than Al-PMOF in lower pressure regions, with a decrease of NH3 adsorbed amounts observed between first cycle and second cycle measurements. Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) suggested that the strength of the Brønsted acidic -OH sites was controlled by the identity of the metal, which resulted in stronger interactions between ammonia and the framework in Ga-PMOF and In-PMOF compared to Al-PMOF

Revolutionizing Future Healthcare using Wireless on the Walls (WoW)

Following the standardization and deployment of fifth generation (5G) network, researchers have shifted their focus to beyond 5G communication. Existing technologies have brought forth a plethora of applications that could not have been imagined in the past years. Beyond 5G will enable us to rethink the capability, it will offer in various sectors including agriculture, search and rescue and more specifically in the delivery of health care services. Unobtrusive and non-invasive measurements using radio frequency (RF) sensing, monitoring and control of wearable medical devices are the areas that would potentially benefit from beyond 5G. Applications such as RF sensing, device charging and remote patient monitoring will be a key challenge using millimetre (mmWave) communication. The mmWaves experience multi-path induced fading, where the rate of attenuation is larger as compared to the microwaves. Eventually, mmWave communication systems would require range extenders and guided surfaces. A proposed solution is the use of intelligent reflective surfaces, which will have the ability to manipulate electromagnetic (EM) signals. These intelligent surfaces mounted and/or coated on walls aka - Intelligent Walls are planar and active surfaces, which will be a key element in beyond 5G and 6G communication. These intelligent walls equipped with machine learning algorithm and computation power would have the ability to manipulate EM waves and act as gateways in the heterogeneous network environment. The article presents the application and vision of intelligent walls for next-generation healthcare in the era of beyond 5G.Comment: 6 pages, 3 figure

High-Throughput Experimentation, Theoretical Modeling, and Human Intuition: Lessons Learned in Metal-Organic Framework-Supported Catalyst Design

We have screened an array of 23 metals deposited onto the metal–organic framework (MOF) NU-1000 for propyne dimerization to hexadienes under different reaction conditions for a total of ~1400 experiments. By a first-of-its-kind study utilizing data-driven algorithms and high-throughput experimentation (HTE) in MOF catalysis, yields on Cu-deposited NU-1000 were improved from 4.2% to 24.4%. Characterization of the most-performant catalysts reveal conversion to hexadiene to be due to the formation of large Cu nanoparticles, which is further supported by reaction mechanisms calculated with density functional theory (DFT). Our results demonstrate both the strengths and weaknesses of the HTE approach. As a strength, HTE excels at being able to find interesting and novel catalytic activity; any a priori theoretical approach would be hard-pressed to find success, as high-performing catalysts required highly specific operating conditions difficult to model theoretically, and initial naïve single-atom models of the active site did not prove representative of the nanoparticle catalysts responsible for conversion to hexadiene. As a weakness, our results show how the HTE approach must be designed and monitored carefully to find success; in our initial campaign (~six months and over half of the total experiments conducted) only minor catalytic performances (up to 4.2% yield) were achieved, which was only improved following a complete overhaul of our HTE approach and questioning our initial assumptions. Thus, the HTE approach is much less automated than it may seem, even if driven by machine learning algorithms – one must carefully design their HTE campaign to find success

Electrophilic Organoiridium(III) Pincer Complexes on Sulfated Zirconia for Hydrocarbon Activation and Functionalization

Single-site supported organometallic catalysts bring together the favorable aspects of homogeneous and heterogeneous catalysis while offering opportunities to investigate the impact of metal–support interactions on reactivity. We report a (dmPhebox)Ir(III) (dmPhebox = 2,6-bis(4,4-dimethyloxazolinyl)-3,5-dimethylphenyl) complex chemisorbed on sulfated zirconia, the molecular precursor for which was previously applied to hydrocarbon functionalization. Spectroscopic methods such as diffuse reflectance infrared Fourier transformation spectroscopy (DRIFTS), dynamic nuclear polarization (DNP)-enhanced solid-state nuclear magnetic resonance (SSNMR) spectroscopy, and X-ray absorption spectroscopy (XAS) were used to characterize the supported species. Tetrabutylammonium acetate was found to remove the organometallic species from the surface, enabling solution-phase analytical techniques in conjunction with traditional surface methods. Cationic character was imparted to the iridium center by its grafting onto sulfated zirconia, imbuing high levels of activity in electrophilic C–H bond functionalization reactions such as the stoichiometric dehydrogenation of alkanes, with density functional theory (DFT) calculations showing a lower barrier for β-H elimination. Catalytic hydrogenation of olefins was also facilitated by the sulfated zirconia-supported (dmPhebox)Ir(III) complex, while the homologous complex on silica was inactive under comparable conditions