Near infrared spectroscopy to predict bitter pit development in different varieties of apples

Bitter pit is a physiological disorder in apples. Several major apple varieties are susceptible to this disorder that poses a great challenge to growers and the associated industry as it significantly reduces the produce utilization value and marketability. The current method of bitter pit detection is through visual assessment of symptoms. Near infrared (NIR) spectroscopy is a non-invasive technique that can be utilized towards detecting bitter pit development in fruits in pre-/non-symptomatic stages. Therefore, NIR spectra (935–2500 nm) of apples were collected from healthy and bitter pit Honeycrisp, Golden Delicious, and Granny Smith apples from a commercial orchard. The apples were stored in a controlled environment and spectral reflectance data were acquired at days 0, 7, 14, 35, and 63 after harvest. Chemical analysis was performed at the end of the storage period to estimate calcium, magnesium, and potassium content in the fruit peel. Partial least square regression (PLSR) was used to identify the apples as healthy or bitter pit using NIR-based spectral features. In addition, specific spectral features were selected by implementing two feature extraction methods: PLSR and stepwise discriminant analysis (SDA) on day 63 spectral dataset. The PLSR and SDA-based selected features from day 63 in Honeycrisp apples classified the same dataset with classification accuracies of about 100% with both methods. Regression analysis indicated a strong relationship between the PLSR-based spectral features and magnesium-to-calcium ratio in fruit peel in all three (Honeycrisp, Golden Delicious, and Granny Smith) apple varieties.     

Two-Dimensional Siloxene–Graphene Heterostructure-Based High-Performance Supercapacitor for Capturing Regenerative Braking Energy in Electric Vehicles

The development of high-performance electrodes that increase the energy density of supercapacitors (SCs) (without compromising their power density) and have a wide temperature tolerance is crucial for the application of SCs in electric vehicles. Recent research has focused on the preparation of multicomponent materials to form electrodes with enhanced electrochemical properties. Herein, a siloxene–graphene (rGO) heterostructure electrode-based symmetric SC (SSC) is designed that delivers a high energy density (55.79 Wh kg⁻¹) and maximum power density of 15 000 W kg⁻¹. The fabricated siloxene–rGO SSC can operate over a wide temperature range from –15 to 80 °C, which makes them suitable for applications in automobiles. This study shows the practical applicability of siloxene–rGO SSC to drive an electric car as well as to capture the braking energy in a regenerative brake-electric vehicle prototype. This work opens new directions for evaluating the use of siloxene–rGO SSC as suitable energy devices in electric vehicles.     

Hybrid FGWO Based FLCs Modeling for Performance Enhancement in Wireless Body Area Networks

Wireless Personal Communications - The progression over wireless technologies paves the way for the emergence of wireless body area networks (WBAN) towards several motivating applications....     

Semianalytical method of solution for solid phase diffusion in lithium ion battery electrodes: Variable diffusion coefficient

A semianalytical methodology based on the integral transform technique is proposed to solve the diffusion equation with concentration dependent diffusion coefficient in a spherical intercalation electrode particle. The method makes use of an integral transform pair to transform the nonlinear partial differential equation into a set of ordinary differential equations, which is solved with less computational efforts. A general solution procedure is presented and two illustrative examples are used to demonstrate the usefulness of this method for modeling of diffusion process in lithium ion battery electrode. The solutions obtained using the method presented in this study are compared to the numerical solutions.     

Arene ruthenium(II) p-chloroacetophenone phenylthiosemicarbazone complex mediated transfer hydrogenation of ketones

A series of cationic half-sandwich arene ruthenium(II) complexes of general formula [Ru(η⁶-p-cymene)Cl(L)]Cl have been synthesized from the reaction of [Ru(η⁶-p-cymene)Cl₂]₂ with thiosemicarbazone derivatives (L). Characterization of the complexes were accomplished by analytical and spectral (FT-IR, UV–Vis, ¹H NMR) methods. Single crystal structure determination reveals the presence of a pseudooctahedral three-legged piano stool conformation. All the complexes exhibit a quasi-reversible one electron reduction in the range from ?0.75 to ?0.85 V. Further, the catalytic activity of the titled complex has been investigated in the transfer hydrogenation of ketones in the presence of isopropanol/NaOH.     

Polyhydroxybutyrate production accompanied by the effective reduction of chemical oxygen demand (COD) and biological oxygen demand (BOD) from industrial effluent

Industrial effluents are major pollution-causing agents for our environment. Our study focuses on utilizing effluents from different industries for efficient production of Polyhydroxybutyrate (PHB). Presence of PHB was identified by Sudan Black staining method. The PHB production parameters for Pseudomonas aeruginosa MTCC 4673 were studied critically, and it was found that glucose with 8.5 mg/L (0.0550 g PHB/g substrate) PHB concentration yielded the highest among the carbon sources used. Peptone with 8.9 mg/L (0.0524 g PHB/g substrate) of PHB concentration, an incubation period of 48 h and at a pH of 7 yielded the optimum results. These studies were compared with those of Alcaligens latus MTCC 2311. Dairy effluents (DE) and tannery effluents (TE) were considered for the best possible substrate, for the production of PHB in an optimized media. The results indicated that the dairy effluents gave a higher yield of PHB. Amongst various dilution levels studied from 10–100% (v/v), 50% (v/v) concentration of the dairy effluent showed maximum PHB productivity of 0.0582 g PHB/g substrate. A comparison of the chemical oxygen demand (COD) and biological oxygen demand (BOD) from the results, showed a significant removal percentage of 78.97% BOD and 53.482% COD, which highlighted the importance of utilizing effluents for PHB production, in order to reduce the risk of toxic effluent discharge. FT-IR analysis was carried out to confirm the presence of PHB.     

The Cytokine Mediated Molecular Pathophysiology of Psoriasis and Its Clinical Implications

Psoriasis is the result of uncontrolled keratinocyte proliferation, and its pathogenesis involves the dysregulation of the immune system. The interplay among cytokines released by dendritic, T_h1, T_h2, and T_h17 cells leads to the phenotypical manifestations seen in psoriasis. Biological therapies target the cytokine-mediated pathogenesis of psoriasis and have improved patient quality of life. This review will describe the underlying molecular pathophysiology and biologics used to treat psoriasis. A review of the literature was conducted using the PubMed and Google Scholar repositories to investigate the molecular pathogenesis, clinical presentation, and current therapeutics in psoriasis. Plaque psoriasis’, the most prevalent subtype of psoriasis, pathogenesis primarily involves cytokines TNF-α, IL-17, and IL-23. Pustular psoriasis’, an uncommon variant, pathogenesis involves a mutation in IL-36RN. Currently, biological therapeutics targeted at TNF-α, IL-12/IL-23, IL-17, and IL-23/IL-39 are approved for the treatment of moderate to severe psoriasis. More studies need to be performed to elucidate the precise molecular pathology and assess efficacy between biological therapies for psoriasis. Psoriasis is a heterogenous, chronic, systemic inflammatory disease that presents in the skin with multiple types. Recognizing and understanding the underlying molecular pathways and biological therapeutics to treat psoriasis is important in treating this common disease.     

UAS imaging-based decision tools for arid winter wheat and irrigated potato production management

Small unmanned aerial systems (UAS) are gaining global attention for rapid image-based decision making in agricultural production. In this study, the aim was to evaluate UAS-based imagery for rapid assessment of wheat winter survival and spring stand in winter wheat production and crop necrosis in potato production. Both are critical aspects of field (arid) and row (irrigated) crop farming practices. Aerial images from 97 hard and 352 soft single nucleotide polymorphism winter wheat plots, and 32 potato field plots (with 1 and 2 years of green manure applications) were acquired using a multi-band imaging sensor integrated with UAS. The UAS-based imagery was useful in evaluating winter wheat plant winter survival and spring stand, with Pearson correlation coefficient (r) in the range 0.60–0.82 between imagery and ground reference data. Similarly, the image-based potato field necrosis assessment showed a strong relationship with ground reference data (r = 0.93 and 0.88 for 1 and 2 years of green manure application, respectively). Overall, UAS imagery provided quantifiable, timely, and unbiased field data with high spatial resolution (about 2.3 cm/pixel for images acquired at 100 m altitude) that can aid in field and row crop production decision making.     

Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms

Basal stem rot (BSR) is a fatal fungal (Ganoderma) disease of oil palm plantations and has a significant impact on the production of palm oil in Malaysia. Because there is no effective treatment to control this disease, early detection of BSR is vital for sustainable disease management. The limitations of visual detection have led to an interest in the development of spectroscopically based detection techniques for rapid diagnosis of this disease. The aim of this work was to develop a procedure for early and accurate detection and differentiation of Ganoderma disease with different severities, based on spectral analysis and statistical models. Reflectance spectroscopy analysis ranging from the visible to near infrared region (325–1075 nm) was applied to analyse oil palm leaf samples of 47 healthy (G0), 55 slightly damaged (G1), 48 moderately damaged (G2), and 40 heavily damaged (G3) trees in order to detect and quantify Ganoderma disease at different levels of severity. Reflectance spectra were pre-processed, and principal component analysis (PCA) was performed on different pre-processed datasets including the raw dataset, first derivative, and second derivative datasets. The classification models: linear and quadratic discrimination analysis, k-nearest neighbour (kNN), and Naïve–Bayes were applied to PC scores for classifying four levels of stress in BSR-infected oil palm trees. The analysis showed that the kNN-based model predicted the disease with a high average overall classification accuracy of 97% with the second derivative dataset. Results confirmed the usefulness and efficiency of the spectrally based classification approach in rapid screening of BSR in oil palm.     

Noise analysis and massive MIMO modelling in VLC for 5G networks using EKF with SCFDM

Visible Light Communications (VLC) is the type of communication, which processes high-speed data transmission using the visible Light Emitting Diodes (LED). The VLC acts as an important supplementary that is used to define the hotspots for heterogeneous networks and plays an important role for 5G networks in wireless communications. However, performance of visible light systems is affected by various noises and Allan variance is used to analyze such noises in 5G networks. The Massive Multiple-Input and Multiple-Output (M-MIMO) technique is used for noise modeling which utilizes the mitigation circuit to find whether the noise is white noise, shot noise, random walk noises or typical noises. The existing Kalman Filter approach failed to attain the required bandwidth and higher spectral efficiency. Therefore, to achieve high data rates, the spectral efficient technologies such as Single Carrier Frequency Division Multiplexing (SCFDM) is performed in the research. The Allan Variance is utilized for analyzing the time-series that extracts the noise features of the data and the major noise is verified and considered by the M-MIMO technique. The present research uses the Extended Kalman Filter (EKF) which determines the observation models and the state transition that does not need linear functions to define the states. The proposed SCFDM was constructed based on the VLC for 5G networks that analyzes in terms of Bit Error Rate (BER) and Signal to Noise Ratio (SNR). The proposed SCFDM obtains a high SNR of 14% for the channels with white LED option when compared to the existing methods.