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Improving fruit quality and reducing pre-harvest fruit drop are critical goals for Citrus reticulata production in Pakistan, where climatic and nutritional challenges affect yield and juice quality. This study evaluated the combined effects of plant growth regulators (salicylic acid and indole acetic acid) and nutrients (potassium and zinc) on fruit drop and juice volume in Citrus reticulata L. Field trials were conducted at three locations in Punjab, Pakistan (Layyah, Faisalabad, and Sargodha) using a randomized complete block design (RCBD) with five replications per treatment. Nutrients (K and Zn at 100 mg/L each) and growth regulators (SA at 100 mg/L and IAA at 5 mg/L) were applied individually or in combination at three growth stages. Statistical analyses, including PCA, ANOVA, and GGE biplot, were used to identify the most effective treatments for improving fruit juice quality and reducing fruit drop. The combined foliar application of SA + K + Zn was the most effective across all parameters, except fruit drop, juice citric acid contents, and juice pH, which were negatively affected. The highest juice potassium content was observed with K application. The PCA and GGE biplot analysis indicated that the Sargodha orchard performed best, with the SA + K treatment being the most effective there, while SA + K + Zn showed the best results at Layyah and Faisalabad for reducing fruit drop, enhancing juice volume, and improving fruit quality. However, individual fruit, juice, and juice nutrient contents traits analyses revealed that the most significant improvements in fruit and juice quality were observed at the Sargodha site instead of Layyah and Faisalabad. The treatment SA + K + Zn proved to be the most stable and consistent in enhancing citrus fruit and juice quality across all three selected locations. The findings suggest that adopting the SA + K + Zn treatment could be a practical approach for citrus farmers aiming to enhance crop yield and fruit quality, thereby supporting agricultural productivity and export potential in Pakistan. [ABSTRACT FROM AUTHOR]

On 11 February 2020, the prevalent outbreak of COVID-19, a coronavirus illness, was declared a global pandemic. Since then, nearly seven million people have died and over 765 million confirmed cases of COVID-19 have been reported. The goal of this study is to develop a diagnostic tool for detecting COVID-19 infections more efficiently. Currently, the most widely used method is Reverse Transcription Polymerase Chain Reaction (RT-PCR), a clinical technique for infection identification. However, RT-PCR is expensive, has limited sensitivity, and requires specialized medical expertise. One of the major challenges in the rapid diagnosis of COVID-19 is the need for reliable imaging, particularly X-ray imaging. This work takes advantage of artificial intelligence (AI) techniques to enhance diagnostic accuracy by automating the detection of COVID-19 infections from chest X-ray (CXR) images. We obtained and analyzed CXR images from the Kaggle public database (4035 images in total), including cases of COVID-19, viral pneumonia, pulmonary opacity, and healthy controls. By integrating advanced techniques with transfer learning from pre-trained convolutional neural networks (CNNs), specifically InceptionV3, ResNet50, and Xception, we achieved an accuracy of 95%, significantly higher than the 85.5% achieved with ResNet50 alone. Additionally, our proposed method, CXR-DNNs, can accurately distinguish between three different types of chest X-ray images for the first time. This computer-assisted diagnostic tool has the potential to significantly enhance the speed and accuracy of COVID-19 diagnoses. [ABSTRACT FROM AUTHOR]

This study presents the production of FAMEs from non-edible Silybum marianum oil using a catalyst consisting of an MXene/SrTiO3 composite. The primary aim of this study was to reduce our reliance on petroleum-based fuels by harnessing non-edible oil sources. The catalyst, once prepared, achieved an impressive conversion rate of 98.8%. The optimal parameters for this catalytic conversion included a 7 wt% catalyst concentration, a 1:12 molar ratio of oil to methanol, a 100 min reaction time, and a reaction temperature of 60 °C. These parameters ensured the successful completion of the FAME conversion process. The physicochemical properties of Silybum marianum oil confirmed its suitability as a biodiesel source on an industrial scale. The verification of the synthesized MXene/SrTiO3 catalyst was conducted via XRD, SEM, EDX, and BET, and synthesized biodiesel was confirmed via 1H and 13C-NMR, FTIR, and GC-MS. These results indicate that the catalyst described in this study exhibits significant potential for cost-effective biodiesel production under the appropriate reaction conditions. [ABSTRACT FROM AUTHOR]

Phosphopentomutases catalyze the isomerization of ribose 1-phosphate and ribose 5-phosphate. Thermococcus kodakarensis, a hyperthermophilic archaeon, harbors a novel enzyme (PPMTk) that exhibits high homology with phosphohexomutases but has no significant phosphohexomutase activity. Instead, PPMTk catalyzes the interconversion of ribose 1-phosphate and ribose 5-phosphate. Here, we report biophysical analysis, crystallization, and three-dimensional structure determination of PPMTk by X-ray diffraction at 2.39 Å resolution. The solved structure revealed a novel catalytic motif, unique to PPMTk, which makes this enzyme distinct from the homologous counterparts. We postulate that this novel catalytic motif may enable PPMTk to isomerize phosphopentose instead of phosphohexose. To the best of our knowledge, this is the first biophysical and structural analysis of a phosphopentomutase from hyperthermophilic archaea. [ABSTRACT FROM AUTHOR]

Designing and developing small organic molecules for use as urease inhibitors is challenging due to the need for ecosystem sustainability and the requirement to prevent health risks related to the human stomach and urinary tract. Moreover, imaging analysis is widely utilized for tracking infections in intracellular and in vivo systems, which requires drug molecules with emissive potential, specifically in the low-energy region. This study comprises the synthesis of a Schiff base ligand and its selected transition metals to evaluate their UV/fluorescence properties, inhibitory activity against urease, and molecular docking. Screening of the symmetrical cage-like ligand and its metal complexes with various eco-friendly transition metals revealed significant urease inhibition potential. The IC50 value of the ligand for urease inhibition was 21.80 ± 1.88 µM, comparable to that of thiourea. Notably, upon coordination with transition metals, the ligand–nickel and ligand–copper complexes exhibited even greater potency than the reference compound, with IC50 values of 11.8 ± 1.14 and 9.31 ± 1.31 µM, respectively. The ligand–cobalt complex exhibited an enzyme inhibitory potential comparable with thiourea, while the zinc and iron complexes demonstrated the least activity, which might be due to weaker interactions with the investigated protein. Meanwhile, all the metal complexes demonstrated a pronounced optical response, which could be utilized for fluorescence-guided targeted drug delivery applications in the future. Molecular docking analysis and IC50 values from in vitro urease inhibition screening showed a trend of increasing activity from compounds 7d to 7c to 7b. Enzyme kinetics studies using the Lineweaver–Burk plot indicated mixed-type inhibition against 7c and non-competitive inhibition against 7d. [ABSTRACT FROM AUTHOR]

The early prediction of ocular disease is certainly an obligatory concern in the domain of ophthalmic medicine. Although modern scientific discoveries have shown the potential to treat eye diseases by using artificial intelligence (AI) and machine learning, explainable AI remains a crucial challenge confronting this area of research. Although some traditional methods put in significant effort, they cannot accurately predict the proper ocular diseases. However, incorporating AI into diagnosing eye diseases in healthcare complicates the situation as the decision-making process of AI demonstrates complexity, which is a significant concern, especially in major sectors like ocular disease prediction. The lack of transparency in the AI models may hinder the confidence and trust of the doctors and the patients, as well as their perception of the AI and its abilities. Accordingly, explainable AI is significant in ensuring trust in the technology, enhancing clinical decision-making ability, and deploying ocular disease detection. This research proposed an efficient transfer learning model for eye disease prediction to transform smart vision potential in the healthcare sector and meet conventional approaches' challenges while integrating explainable artificial intelligence (XAI). The integration of XAI in the proposed model ensures the transparency of the decision-making process through the comprehensive provision of rationale. This proposed model provides promising results with 95.74% accuracy and explains the transformative potential of XAI in advancing ocular healthcare. This significant milestone underscores the effectiveness of the proposed model in accurately determining various types of ocular disease. It is clearly shown that the proposed model is performing better than the previously published methods. [ABSTRACT FROM AUTHOR]

The design and development of fluorescent materials for detecting cancer-related enzymes are crucial for cancer diagnosis and treatment. Herein, we present a substituted rhodamine derivative for the chromogenic and fluorogenic detection of the cancer-relevant enzyme γ-glutamyltranspeptidase (GGT). Initially, the probe is non-chromic and non-emissive due to its spirolactam form, which hinders extensive electronic delocalization over broader pathway. However, selective enzymatic cleavage of the side-coupled group triggers spirolactam ring opening, resulting in electronic flow across the rhodamine skeleton, and reduces the band gap for low-energy electronic transitions. This transformation turns the reaction mixture from colorless to intense pink, with prominent UV and fluorescence bands. The sensor's selectivity was tested against various human enzymes, including urease, alkaline phosphatase, acetylcholinesterase, tyrosinase, and cyclooxygenase, and showed no response. Absorption and fluorescence titration analyses of the probe upon incremental addition of GGT into the probe solution revealed a consistent increase in both absorption and emission spectra, along with intensified pink coloration. The cellular toxicity of the receptor was evaluated using the MTT assay, and bioimaging analysis was performed on BHK-21 cells, which produced bright red fluorescence, demonstrating the probe's excellent cell penetration and digestion capabilities for intracellular analytical detection. Molecular docking results supported the fact that probe-4 made stable interactions with the GGT active site residues. [ABSTRACT FROM AUTHOR]

The behavior of wheat straw biomass (WS), woody sawdust biomass (WB), and their blends during catalytic co-pyrolysis are analyzed in the presence of CaO catalyst, which is obtained from the calcination of mussel shells. Synergy analysis of blends and pure materials is measured by studying the difference between theoretical and experimental values of wt.%/min, (RL%), and (WL%), which correspond to maximum weight loss rate, residue left, and weight loss, respectively. The Coats–Redfern method is utilized for evaluating the thermo-kinetic properties. The chemical reaction order model F1 is the best model that describes the Ea of 60.05 kJ/mol and ∆H, ∆G, and ∆S values of 55.03 kJ/mol, 162.26 kJ/mol, and −0.18 kJ/mol.K, respectively, for the optimum blend 80WS−20WB, reducing the thermo-kinetic properties. Model D3 showed better results for the Ea, ∆H, ∆G, and ∆S for the 5% CaO blend, which certified the viability of co-pyrolysis of WS and WB, while DTG indicated that exothermic and endothermic reactions occur together. [ABSTRACT FROM AUTHOR]

The decarbonization of transportation plays a crucial role in mitigating climate change, and biodiesel has emerged as a promising solution due to its renewable and eco-friendly nature. However, in order to maintain the momentum of the "green trend" and ensure energy security, an ecologically friendly pathway is important to produce efficient biodiesel. In this work, activated carbon (AC) obtained from rice husk (RH) is hydrothermally prepared and modified through cobalt transition metal for catalyst support for the transesterification process. The physicochemical characteristics of the synthesized catalysts are examined using XRD, FTIR, SEM and EDS, TGA, and BET, while the produced biodiesel is also characterized using Gas Chromatography and Mass Spectroscopy (GC-MS). To optimize the transesterification process, Fatty Acid Methyl Esters (FAME) are produced by the conversion of waste cooking oil. Response Surface Methodology (RSM) is used to validate temperature (75 °C), the methanol-to-oil molar ratio (1:9), catalyst weight percentage (2 wt.%), and retention time (52.5 min). The highest conversion rate of waste cooking oil (WCO) to biodiesel was recorded at 96.3% and tested as per American Society for Testing and Materials (ASTM) standards. Based on the results, it is clear that cobalt-loaded rice husk-based green catalyst (RHAC-Co) enhanced catalytic activity and yield for biodiesel production. Further research should focus on engine performance evaluation and scaling up of the catalyst by optimizing it for the industrial scale. [ABSTRACT FROM AUTHOR]

Climate change is an important concern worldwide. This huge change is causing a negative impact on crop productivity throughout the whole world and shows some drastic effects on sensitive crops including sweet pepper and other kinds of chilies. These stresses have a negative effect on vegetable growth, fruit quality, and fruit yield. Besides these stresses, accurate planting time is the utmost factor in increasing the crop potential and its productivity. Therefore, this study aimed to evaluate the optimal planting date and best-performing cultivar for sweet pepper to maximize crop productivity, growth, yield, and fruit shelf life under the impacts of climate change. The experiment was conducted in Multan, Pakistan (30° 25 N and 71° 30 E). In this experiment, four planting dates (10 January, 25 January, 10 February, and 25 February) with three cultivars (Ganga, Winner, and Savio) were grown with three replications (each year) during 2020 and 2021. Our findings showed that plant growth, fresh and dry biomass, yield, and yield attributes were statistically higher in earlier plantings, and lower in delayed planting. Regarding the cultivars in both years, 'Winner' performed better in all growth and yield parameters as compared to other growing cultivars. Lower weight loss and decay incidence were reported in 'Winner' cultivar. Additionally, 'Winner' cultivar showed a higher sensory score as compared to the other cultivar in both years. From the results, we conclude that the nursery of sweet pepper plants should be planted on the 25 January to attain higher crop productivity, and the cultivar 'Winner' showed promising effects so it should be planted for better yield. [ABSTRACT FROM AUTHOR]

We propose a new definition of the γ -convex stochastic processes (C SP) using center and radius (CR) order with the notion of interval valued functions (C. R I. V) . By utilizing this definition and Mean-Square Fractional Integrals, we generalize fractional Hermite–Hadamard–Mercer-type inclusions for generalized   C. R I. V versions of convex, tgs-convex, P-convex, exponential-type convex, Godunova–Levin convex, s-convex, Godunova–Levin s-convex, h-convex, n-polynomial convex, and fractional n-polynomial (C SP) . Also, our work uses interesting examples of   C. R I. V (C SP) with Python-programmed graphs to validate our findings using an extension of Mercer's inclusions with applications related to entropy and information theory. [ABSTRACT FROM AUTHOR]

The rising frequency of natural disasters demands efficient and accurate structural damage assessments to ensure public safety and expedite recovery. Human error, inconsistent standards, and safety risks limit traditional visual inspections by engineers. Although UAVs and AI have advanced post-disaster assessments, they still lack the expert knowledge and decision-making judgment of human inspectors. This study explores how expertise shapes human–building interaction during disaster inspections by using eye tracking technology to capture the gaze patterns of expert and novice inspectors. A controlled, screen-based inspection method was employed to safely gather data, which was then used to train a machine learning model for saliency map prediction. The results highlight significant differences in visual attention between experts and novices, providing valuable insights for future inspection strategies and training novice inspectors. By integrating human expertise with automated systems, this research aims to improve the accuracy and reliability of post-disaster structural assessments, fostering more effective human–machine collaboration in disaster response efforts. [ABSTRACT FROM AUTHOR]

Rising population levels exert significant pressure on available freshwater resources. Scientists and researchers from various countries are diligently seeking a long-lasting solution using solar-powered desalination. This research paper investigates the current advancements in solar desalination research by utilizing the method of "scientometrics". Scientometrics employs traditional methodologies, including bibliometrics, which entails quantifying the number of research papers published, and citation analysis, which involves examining the frequency with which other researchers cite these papers. By integrating these two approaches, scientometrics provides invaluable information about the most influential countries, institutions, and individual researchers in the field. Utilizing the software program VOSviewer, a comprehensive analysis was conducted on 1855 research papers published between 2010 and 2024. These papers were selected based on a predetermined set of ten key search terms. The results of the analysis indicate that China is the leading country in this field, as it boasts the highest number of published papers and the most citations received overall. Notably, Egyptian research institutions have been identified as the most influential in this area. Moreover, a single author has notably amassed 3419 citations for their 54 published works on solar desalination. This analysis unveiled past and contemporary advancements in the field and identified current trends through keyword analysis. It also offers recommendations based on bibliometric findings, including suggestions for addressing the challenges faced by solar-derived systems and addressing research area saturation. [ABSTRACT FROM AUTHOR]

Soil pollution caused by heavy metal(oid)s has generated great concern worldwide due to their toxicity, persistence, and bio-accumulation properties. To assess the baseline data, the heavy metal(oid)s, including manganese (Mn), iron (Fe), Cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), lead (Pb), mercury (Hg), chromium (Cr), and cadmium (Cd), were evaluated in surface soil samples collected from the farmlands of Grand Forks County, North Dakota. Samples were digested via acid mixture and analyzed via inductively coupled plasma mass spectrometry (ICP MS) analysis to assess the levels, ecological risks, and possible sources. The heavy metal(oid) median levels exhibited the following decreasing trend: Fe > Mn > Zn > Ni > Cr > Cu > Pb > Co > As > Cd > Hg. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) suggested the main lithogenic source for the studied metal(oid)s. Metal(oid) levels in the current investigation, except Mn, are lower than most of the guideline values set by international agencies. The contamination factor (Cf), geo accumulation index (Igeo) and enrichment factor (EF) showed considerable contamination, moderate contamination, and significant enrichment, respectively, for As and Cd on median value basis. Ecological risk factor (Er) results exhibited low ecological risk for all studied metal(oid)s except Cd, which showed considerable ecological risk. The potential ecological risk index (PERI) levels indicated low ecological risk to considerable risk. Overall, the results indicate the accumulation of As and Cd in the study area. The high nutrients of the soils potentially affect their accumulation in crops and impact on consumers' health. This drives the impetus for continued environmental monitoring programs. [ABSTRACT FROM AUTHOR]

Complex systems are prevalent in various disciplines encompassing the natural and social sciences, such as physics, biology, economics, and sociology. Leveraging data science techniques, particularly those rooted in artificial intelligence and machine learning, offers a promising avenue for comprehending the intricacies of complex systems without necessitating detailed knowledge of underlying dynamics. In this paper, we demonstrate that multiscale entropy (MSE) is pivotal in describing the steady state of complex systems. Introducing the multiscale entropy dynamics (MED) methodology, we provide a framework for dissecting system dynamics and uncovering the driving forces behind their evolution. Our investigation reveals that the MED methodology facilitates the expression of complex system dynamics through a Generalized Nonlinear Schrödinger Equation (GNSE) that thus demonstrates its potential applicability across diverse complex systems. By elucidating the entropic underpinnings of complexity, our study paves the way for a deeper understanding of dynamic phenomena. It offers insights into the behavior of complex systems across various domains. [ABSTRACT FROM AUTHOR]

Convexity plays a crucial role in the development of fractional integral inequalities. Many fractional integral inequalities are derived based on convexity properties and techniques. These inequalities have several applications in different fields such as optimization, mathematical modeling and signal processing. The main goal of this article is to establish a novel and generalized identity for the Caputo–Fabrizio fractional operator. With the help of this specific developed identity, we derive new fractional integral inequalities via exponential convex functions. Furthermore, we give an application to some special means. [ABSTRACT FROM AUTHOR]

Metallic nanoparticles are very useful, effective, and usually synthesized by toxic and expensive chemicals. Silver nanoparticles (AgNPs), measuring less than 100 nm, have shown promising impact in several biomedical investigations. These can inhibit microbial growth and aid in medicine administration. Six substrates of Carica papaya were used to synthesize silver nanoparticles that can limit the growth of bacteria and fungi. In this article, we report the synthesis of AgNPs from the leaf, seed, callus, peel, fruit juice, and bark of Carica papaya. AgNPs synthesized from callus showed the most promising results when tested against the growth of bacteria like Xanthomonas campestris, Erwinia carotovera, Bacillus subtilis, and fungi (Aspergillus niger and Fusarium oxysporum) when compared with other extracts' efficacy, and the callus was regenerated from petiole and midrib explants of Carica papaya in MS basal media supplemented with NAA and Kinetin (1 + 0.5 mg/L). A ratio of 1:20 of substrate extract to 1 mM AgNO3 produced the most effective nanoparticles in terms of capping, quality, and stability when tested through surface plasmon resonance (SPR) within the 400–435 nm range. The nanoparticle sizes of all six types were measured using Image J software on micrographs of SEM at 200 nm resolution. The average diameters were analyzed through Origin software, and the finest AgNPs were observed to be synthesized from callus extract, i.e., 18.91 nm with rod-like morphology. Energy dispersive X-ray (EDX) at 2.6 keV revealed 43.38, 75.39, 70.611, 36.54, 58.57, and 45.94 percent elemental silver in AgNPs formed from the leaf, callus, juice, seed, bark, and peel extract, respectively. Silver nanoparticles synthesized from callus extract were smaller and exhibited the most effective antimicrobial potential, with the highest inhibitory zone of 19 mm against Xanthomonas campestris bacterium and up to 14 mm against Aspergillus niger fungus. Furthermore, the percentage of elemental Ag (measured through EDX) was found to be highest in the nanoparticles synthesized from callus compared to those synthesized from the leaf, seed, peel, fruit juice, and bark of Carica papaya. Hence, the callus extract is the most suitable substrate for the reduction of silver nitrate solution in 1:20 to form the finest silver nanoparticles in an effective biogenic way. [ABSTRACT FROM AUTHOR]

Ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1) regulates skeletal and soft tissue mineralization by hydrolyzing nucleotide triphosphates and cyclic nucleotides, and is involved in the modulation of immune system. In fact, ENPP1 degrades 2′,3′-cyclic GMP-AMP dinucleotide (2′,3′-cGAMP), which is an agonist of surface receptor stimulator of interferon genes (STING), thus downregulating immune response. Consequently, ENPP1 inhibitors are being studied as adjuvant agents in infections and cancer. Pistacia chinensis is a medicinal plant endowed with several biological activities and traditional uses. In the current study, we report the isolation of transilitin (2-(3,4-dihydroxyphenyl)-7,8-dihydroxy-3-methoxychromen-4-one) from the methanolic extract of P. chinensis barks and the investigation of its activity as ENPP1 inhibitor. The compound was tested in vitro against snake venom phosphodiesterase, which is structurally related to ENPP1, and dose-dependently inhibited the enzyme. Moreover, molecular modeling studies were employed to assess the binding motif of the transilitin with the macromolecular target. Our findings support the traditional medical application of P. chinensis and its extracts by shedding new light on the mechanisms underlying their biological action. [ABSTRACT FROM AUTHOR]

Mineral oil resources are depleting rapidly, and the slower conventional oil biodegradation process results in environmental pollution. To resolve this issue, cupric oxide (CuO) nanoparticles (1% wt) were introduced into a base oil to improve the lubricating capability of castor oil. In addition, 1% wt. sodium dodecyl sulfate was also blended with the base oil in order to attain the maximum dispersion stability of CuO nanoparticles in the castor oil. Afterward, thermophysical property, atomic absorption spectroscopy, and Fourier transform infrared radiation (FTIR) testing of the lubricant oil sample were performed before and after 100 h of engine operations at 75% throttle and 2200 rpm for each lubricant sample in order to check the capability of the novel oil with mineral oil. Compared with the natural mineral oil, the behavior of the CuO-based lubricant has essentially the same physical features, as measured according to ASTM standard methods. The physicochemical properties like (KV)40 °C, (KV)100 °C, FP, ash, and TBN decrease more in the case of the synthetic oil by 1.15, 1.11, 0.46, 1.1, and 1.2% than in the conventional oil, respectively. FTIR testing shows that the maximum peaks lie in the region of 500 to 1750 cm−1, which shows the presence of C=O, C-N, and C-Br to a maximum extent in the lubricant oil sample. AAS testing shows that the synthetic oil has 21.64, 3.23, 21.44, and 1.23% higher chromium, iron, aluminum, and zinc content. However, the copper and calcium content in the synthetic oil is 14.72 and 17.68%, respectively. It can be concluded that novel bio-lubricants can be utilized as an alternative to those applications that are powered by naturally produced mineral oil after adding suitable additives that further enhance their performance. [ABSTRACT FROM AUTHOR]

Solar energy is a green and sustainable source of energy which can be converted to electrical power through photovoltaic (PV) panels. In an electrical network system, electrical energy is supplied to end consumers through utility networks. Smart Grids allow consumers to generate electrical energy locally and surplus energy at the user's end can be supplied to the national grid. Bidirectional energy meters keep the record of the imported and exported units to the grid and vice versa. Photovoltaic panels cannot generate power at night, so users have to import power from electric grid. The utility charges the consumer monthly as per net balance of the energy meter depending on the energy trade balance. This IoT-based meter allows real-time monitoring of power ratings via the Android application. [ABSTRACT FROM AUTHOR]

The process of the aqueous synthesis of nanomaterials has gained considerable interest due to its ability to eliminate the need for complex organic solvents, which aligns with the principles of green chemistry. Fabricating nanostructures in aqueous solutions has gained recognition for its potential to develop ultrasensitive, low-energy, and ultrafast optoelectronic devices. This study focuses on synthesizing lead iodide (PbI2) nanoplates (NPs) using a water-based solution technique and fabricating a planar photodetector. The planar photodetectors (ITO/PbI2 NPs/Au) demonstrated a remarkable photosensitivity of 3.9 × 103 and photoresponsivity of 0.51 mA/W at a wavelength of 405 nm. Further, we have carried-out analytical calculations for key performance parameters including open-circuit voltage (Voc), short-circuit current (Isc), on-off ratio, responsivity (R), and specific detectivity (D*) at zero applied bias, while photodetector operating in self-powered mode. These values are as follows: Voc = 0.103 V, Isc = 1.93 × 10−8, on-off ratio = 103, R = 4.0 mA/W, and D* = 3.3 × 1011 Jones. Particularly, the asymmetrical output properties of ITO/PbI2 NPs/Au detector provided additional evidence of the effective creation of a Schottky contact. Therefore, the photodetector exhibited a photo-response even at 0 V bias (rise/decay time ~1 s), leading to the realization of self-powered photodetectors. Additionally, the device exhibited a rapid photo-response of 0.23/0.38 s (−5 V) in the visible range. This study expands the scope of aqueous-phase synthesis of PbI2 nanostructures, enabling the large-area fabrication of high-performance photodetectors. [ABSTRACT FROM AUTHOR]

Manufacturing of dies, molds, and their allied components requires the machining of holes with different profiles. Electric discharge machining (EDM) die-sinking is a crucial process used in the dies and molds manufacturing industry. By nature, EDM die-sinking is a relatively slow process in terms of material removal rate (MRR) and there are high amounts of tool material loss in terms of tool wear rate (TWR) which directly influence dimensional accuracies and surface roughness (SR). Therefore, the process is continuously evolving to address these limitations. The present research is aligned in this direction such as to bring improvements in MRR, TWR, and SR through modifications to the conventional electrode design and its geometrical parameters. Traditional designs of EDM electrodes have a uniform cross-section through the tool's entire length and have only one geometrical parameter, i.e., the tool's cross-section. To improve the EDM performance, traditional designs are completely modified by introducing several geometrical parameters such as relief angles, land thickness, cross-sectional area, shank height, circular relief, and non-circular relief, etc. Electrode designs are employed to mill non-circular profiles including triangular, square, and hexagonal shaped holes. The EDM performance measures strongly depend on the tool's geometrical parameters (design type, relief angle, land thickness), machining profile (circular, square, triangle, hexagon), as well as the height/depth of the machining feature. By selecting proper tool designs and corresponding geometrical parameters, the EDM performance measures can be improved significantly. [ABSTRACT FROM AUTHOR]

This paper is an attempt to present a simple yet innovative planning method for determining the type of common bus in a hub station (HS), devised on efficiency and reliability grounds. The efficiency is evaluated by modeling the efficiency curves of the converters under part-load and full-load conditions, hence enabling a realistic estimate of the efficiency. Reliability evaluation is executed by modeling the failure and repair rate distributions of the HS components. The optimal common bus type selection is based on both the efficiency and reliability metrics of the HS. The deterministic factor in the type of common bus selection is proved to be the number of components in the HS. The results indicate that DC and AC systems have comparable efficiencies with a meagre difference of 1.26%. The failure rate of AC common bus architecture is 22% greater than DC common bus architecture resulting in a 1.788 times reliability advantage of the DC system. Moreover, the greater number of components in the case of a AC common bus adds to the economic advantage of DC with a lower number of components leading DC to be an optimal design. [ABSTRACT FROM AUTHOR]

The public–private partnership (PPP) based model for the execution of infrastructure projects originated from Anglo-Saxon countries and was initially used in 1977 by the United Kingdom (U.K). Since then, its popularity has increased worldwide. Earlier studies by researchers and many other professional sectors and departments have introduced PPP contracts into different execution modes like Build, Operate, and Transfer (BOT); Build, Own, Operate, and Transfer (BOOT); and Build, Lease, and Transfer (BLT), etc. All definitions of PPP contracts are different but have a few common characteristics and risks. Previously, numerous pieces of literature were available on these common risks for various execution modes of PPP contracts. However, each PPP mode still has unique risks that must be identified to understand and successfully implement the PPP projects properly. This paper fills the gap mentioned above and aims to identify various commonly used PPP execution modes in infrastructure projects and their corresponding risks after placing the different PPP execution modes into four (04) different categories. Identified risks for the corresponding PPP categories were also divided into seven (07) stages of the PPP life cycle. Semi-structured interviews were conducted to gather information from thirty-four (34) PPP experts worldwide. Accordingly, interviews are transcribed and processed for thematic analysis in academic NVIVO software. These identified risks are further placed in the respective PPP category for the convenience and better understanding of the study's outcome to the users and for the subsequent prioritization and allocation of these identified risks accordingly to the PPP parties during the finalization of the PPP execution mode. [ABSTRACT FROM AUTHOR]

Simple Summary: The harmful effects of microplastic (MP) exposure on aquatic animals have been extensively studied; however, there is a lack of research on its impact on poultry. To address this gap, the present study aimed to evaluate the effects of MP exposure on the growth performance and gut microbiota of chickens. The findings of the study revealed that MPs had a significant negative impact on the growth performance of chickens and can cause an imbalance in gut microbiota. As novel environmental contaminants, MPs exist widely in the environment and accumulate in organisms, which has become a global ecological problem. MP perturbations of organismal physiology and behavior have been extensively recorded in aquatic animals, but the potential effects of MPs on poultry are not well characterized. Here, we explored the adverse effects of MP exposure on the growth performance and gut microbiota of chickens. Results showed that the growth performance of chickens decreased significantly during MP exposure. Additionally, Firmicutes, Bacteroidota, and Proteobacteria were found to be dominant in the gut microbiota of MP-exposed chickens, regardless of health status. Although the types of dominant bacteria did not change, the abundances of some bacteria and the structure of the gut microbiota changed significantly. Compared with the controls, the alpha diversity of gut microbiota in chickens exposed to MPs showed a significant decrease. The results of comparative analyses of bacteria between groups showed that the levels of 1 phyla (Proteobacteria) and 18 genera dramatically decreased, whereas the levels of 1 phyla (Cyanobacteria) and 12 genera dramatically increased, during MP exposure. In summary, this study provides evidence that exposure to MPs has a significant impact on the growth performance and gut microbial composition and structure of chickens, leading to a gut microbial imbalance. This may raise widespread public concern about the health threat caused by MP contamination, which is relevant to the maintenance of environmental quality and protection of poultry health. [ABSTRACT FROM AUTHOR]

Numerical simulations have been performed to investigate the influence of constant volumetric heat generation and viscous dissipation on the unsteady natural convection flow of an incompressible Newtonian fluid contained in a rectangular cavity. The left vertical wall of the cavity is cooled, while the right vertical wall is heated, and the bottom and top walls are adiabatic. A numerical technique based on the implicit finite difference method (IFDM), along with an upwind finite difference scheme and an iterative successive over relaxation (SOR) technique, is employed to solve the governing equations numerically. The effect of physical parameters, namely the modified Rayleigh number ( 10 3 ≤ R a ≤ 10 7 ), aspect ratio ( 1 ≤ A ≤ 4 ), Prandtl number ( P r = 0.7 , 1.0 , 6.2 , 15), volumetric internal heat generation parameter ( Q λ = 0 , 1), and Eckert number ( 0 ≤ E c ≤ 10 − 6 ), on the streamlines and isotherms are discussed graphically. Variations of maximum stream function, as well as average and local Nusselt number, are also discussed. The results show that the increase in Eckert number from 0 to 10 − 4 causes the average heat transfer to decrease, while P r = 0.71 , R a = 10 4 , and Q λ = 0 . Additionally, the average heat transfer decreases as the cavity width increases from 1 to 4, while P r = 0.71 , R a = 5 × 10 4 , E c = 10 − 6 and Q λ = 1 . The results of the numerical model used here are in excellent accord with earlier findings. [ABSTRACT FROM AUTHOR]

In this study, bimetallic nanoparticles (NPs) of silver (Ag) and zinc oxide (ZnO) were synthesized using Leptadenia pyrotechnica leaf extract for the first time. Monometallic NPs were also obtained for comparison. The characterization of the prepared NPs was carried out using various techniques, including UV-Visible spectroscopy (UV-Vis), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The latter confirmed the crystalline nature and diameter of the monometallic and bimetallic NPs of Ag and ZnO. The SEM images of the prepared NPs revealed their different shapes. The biological activities of the NPs were evaluated concerning their antibacterial, antioxidant, and cytotoxic properties. The antibacterial activities were measured using the time-killing method. The results demonstrated that both the monometallic and bimetallic NPs inhibited the growth of Gram-negative (Escherichia coli) and Gram-positive (Staphylococcus aureus) bacteria. The antioxidant activities of the NPs were evaluated using the DPPH (2,2-diphenyl-1-picrylhydrazyl) assay and their cytotoxicity was checked using the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. The results indicated that the controlled quantity of the monometallic and bimetallic NPs did not affect the viability of the cells. However, the decreased cell (L-929) viability suggested that the NPs could have anticancer properties. Furthermore, the photocatalytic degradation of methyl violet and 4-nitrophenol was investigated using the prepared Ag/ZnO NPs, examining the factors affecting the degradation process and conducting a kinetic and thermodynamic study. The prepared Ag/ZnO NPs demonstrated good photocatalytic degradation (88.9%) of the methyl violet (rate constant of 0.0183 min−1) in comparison to 4-nitrophenol (NPh), with a degradation rate of 81.37% and 0.0172 min−1, respectively. Overall, the bimetallic NPs showed superior antibacterial, antioxidant, cytotoxic, and photocatalytic properties compared to the monometallic NPs of Ag and ZnO. [ABSTRACT FROM AUTHOR]

Globally, optimized doses of exogenously applied growth regulators hold the potential to sustainably boost the growth and productivity of leguminous crops, including green gram. A field investigation was undertaken at the Agronomy Farm of the University of Agriculture Faisalabad, Pakistan in 2021–2022 to determine the highest-performing doses of foliar-applied salicylic acid (S1 = 0 and S2 = 75 ppm) and gibberellic acid (G1 = 0, G2 = 30, G3 = 60, G4 = 90 and G5 = 120 ppm) for green gram (cv. NIAB-MUNG 2011) sown under irrigated conditions in a semiarid climate. The response variables included physiological growth traits (CGR and net assimilation rate (NAR)), yield attributes (plant height (PH), PBs and the number of pods per plant−1 (NP), pod length (PL) and SW, grain (GY) and biological yields (BY), the biosynthesis of pigments (chlorophyll a, chlorophyll b and total chlorophyll along with carotenoids) and protein (P) contents. The results revealed that S2G5 remained unmatched in that it exhibited the highest crop growth rate, while it remained on par with S2G4 and S2G3 in terms of its net assimilation rate. Additionally, S2G5 maximized plant height, the number of pod-bearing branches and pods per plant, pod length, seed number per pod−1 and 1000-seed weight, which led to the highest grain yield and biological yield (104% and 69% greater than those of the control, respectively). Moreover, the same treatment combination also surpassed the rest of the treatments because it recorded the largest amounts of chlorophyll and carotenoid contents, and the P content was increased to 24% greater than that observed for the control treatment. Thus, the exogenous application of salicylic acid (75 ppm) and gibberellic acid (120 ppm) might be recommended to green gram growers to sustainably increase the plant's yield and nutritional value, and these findings may serve as a baseline for conducting more studies to test higher doses of these growth regulators. [ABSTRACT FROM AUTHOR]

The WRKY transcription factor plays a crucial role in plant stress adaptation. Our research has found that WRKY6 in Solanum tuberosum (potatoes) is closely related to cadmium (Cd) tolerance. Therefore, investigating the mechanism of StWRKY6 in plant resistance to Cd toxicity is of great scientific importance for food safety. This research further analyzed the gene structure and functional regions of the nuclear transcription factor WRKY6 in potatoes, discovering that StWRKY6 contains W box, GB/box, ABRE, and other elements that can act as a nuclear transcription regulatory factor to execute multiple functional regulations. The results of the heterologous expression of StWRKY6 in Arabidopsis under Cd stress showed that the overexpression line (StWRKY6-OE) had significantly higher SAPD values and content of reactive oxygen species scavenging enzymes than the wild type, indicating that StWRKY6 plays a crucial role in protecting the photosynthetic system and promoting carbohydrate synthesis. Transcriptome analysis also revealed that the Cd-induced expression of StWRKY6 up-regulated many potential gene targets, including APR2, DFRA, ABCG1, VSP2, ERF013, SAUR64/67, and BBX20, which are involved in Cd chelation (APR2, DFRA), plant defense (VSP2, PDF1.4), toxic substance efflux (ABCG1), light morphology development (BBX20), and auxin signal (SAUR64/67). These genes coordinate the regulation of Cd tolerance in the StWRKY6 overexpression line. In summary, this study identified a potential gene set of the co-expression module of StWRKY6, providing useful evidence for the remediation of Cd-contaminated soil and the genetic breeding of low Cd-accumulating crops, thereby ensuring food safety. [ABSTRACT FROM AUTHOR]

Phase transitions are an intriguing yet poorly understood aspect of transition-metal-based materials; these phase transitions can result in changes to the refractive index, absorption coefficient, and other optical properties of the materials. Transition-metal-based materials exist in a variety of crystalline phases and also have metallic, semi-metallic, and semi-conducting characteristics. In this review, we demonstrate that alloyed W- and Mo-based dichalcogenides enable phase transitions in structures, with phase transition temperatures that are tunable across a wide range using various alloy models and modern DFT-based calculations. We also analyze the tuning the optical bandgap of the metal oxide nanoparticles through doping of the transition metal in a manner that is suitable for optical switching and thermal imaging. After the introduction and a brief illustration of the structures and their exceptional properties, we discuss synthetic methodologies and their application as part of important strategies toward the enhanced performance of transition-metal-based dichalcogenides and oxides. In the end, our conclusion highlights the prospects of 2D materials as phase transition materials due to their advantages in terms of scalability and adaptability. [ABSTRACT FROM AUTHOR]

Electric discharge machining is relatively a slow process in terms of machining time and material removal rate. The presence of overcut and the hole taper angle caused by the excessive tool wear are other challenges in the electric discharge machining die-sinking process. The areas of focus to solve these challenges in the performance of electric discharge machines include increasing the rate of material removal, decreasing the rate of tool wear, and reducing the rate of hole taper angle and overcut. Triangular cross-sectional through-holes have been produced in D2 steel through die-sinking electric discharge machining (EDM). Conventionally, the electrode with uniform triangular cross-section throughout the electrode length is used to machine triangular holes. In this study, new designs of electrodes (non-conventional designs) are employed by introducing circular relief angles. For material removal rate (MRR), tool wear rate (TWR), overcut, taper angle, and surface roughness of the machined holes, the machining performance of conventional and unconventional electrode designs is compared. A significant improvement in MRR (32.6% increase) has been achieved by using non-conventional electrode designs. Similarly, the hole quality resulted by non-conventional electrodes is way better than hole quality corresponding to conventional electrode designs, especially in terms of overcut and hole taper angle. A reduction of 20.6% in overcut and a reduction of 72.5% in taper angle can be achieved through newly designed electrodes. Finally, one electrode design has been selected (electrode with 20 degree relief angle) as the most appropriate electrode resulting in better EDM performance in terms of MRR, TWR, overcut, taper angle, and surface roughness of triangular holes. [ABSTRACT FROM AUTHOR]

The biosynthesis of algal-based zinc oxide (ZnO) nanoparticles has shown several advantages over traditional physico-chemical methods, such as lower cost, less toxicity, and greater sustainability. In the current study, bioactive molecules present in Spirogyra hyalina extract were exploited for the biofabrication and capping of ZnO NPs, using zinc acetate dihydrate and zinc nitrate hexahydrate as precursors. The newly biosynthesized ZnO NPs were characterized for structural and optical changes through UV-Vis spectroscopy, Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDX). A color change in the reaction mixture from light yellow to white indicated the successful biofabrication of ZnO NPs. The UV-Vis absorption spectrum peaks at 358 nm (from zinc acetate) and 363 nm (from zinc nitrate) of ZnO NPs confirmed that optical changes were caused by a blue shift near the band edges. The extremely crystalline and hexagonal Wurtzite structure of ZnO NPs was confirmed by XRD. The involvement of bioactive metabolites from algae in the bioreduction and capping of NPs was demonstrated by FTIR investigation. The SEM results revealed spherical-shaped ZnO NPs. In addition to this, the antibacterial and antioxidant activity of the ZnO NPs was investigated. ZnO NPs showed remarkable antibacterial efficacy against both Gram-positive and Gram-negative bacteria. The DPPH test revealed the strong antioxidant activity of ZnO NPs. [ABSTRACT FROM AUTHOR]

Cesium lead iodide (CsPbI3) perovskite nanocrystals (NCs) are a promising material for red-light-emitting diodes (LEDs) due to their excellent color purity and high luminous efficiency. However, small-sized CsPbI3 colloidal NCs, such as nanocubes, used in LEDs suffer from confinement effects, negatively impacting their photoluminescence quantum yield (PLQY) and overall efficiency. Here, we introduced YCl3 into the CsPbI3 perovskite, which formed anisotropic, one-dimensional (1D) nanorods. This was achieved by taking advantage of the difference in bond energies among iodide and chloride ions, which caused YCl3 to promote the anisotropic growth of CsPbI3 NCs. The addition of YCl3 significantly improved the PLQY by passivating nonradiative recombination rates. The resulting YCl3-substituted CsPbI3 nanorods were applied to the emissive layer in LEDs, and we achieved an external quantum efficiency of ~3.16%, which is 1.86-fold higher than the pristine CsPbI3 NCs (1.69%) based LED. Notably, the ratio of horizontal transition dipole moments (TDMs) in the anisotropic YCl3:CsPbI3 nanorods was found to be 75%, which is higher than the isotropically-oriented TDMs in CsPbI3 nanocrystals (67%). This increased the TDM ratio and led to higher light outcoupling efficiency in nanorod-based LEDs. Overall, the results suggest that YCl3-substituted CsPbI3 nanorods could be promising for achieving high-performance perovskite LEDs. [ABSTRACT FROM AUTHOR]

Researchers have proposed several automated diagnostic systems based on machine learning and data mining techniques to predict heart failure. However, researchers have not paid close attention to predicting cardiac patient mortality. We developed a clinical decision support system for predicting mortality in cardiac patients to address this problem. The dataset collected for the experimental purposes of the proposed model consisted of 55 features with a total of 368 samples. We found that the classes in the dataset were highly imbalanced. To avoid the problem of bias in the machine learning model, we used the synthetic minority oversampling technique (SMOTE). After balancing the classes in the dataset, the newly proposed system employed a χ 2 statistical model to rank the features from the dataset. The highest-ranked features were fed into an optimized random forest (RF) model for classification. The hyperparameters of the RF classifier were optimized using a grid search algorithm. The performance of the newly proposed model ( χ 2 _RF) was validated using several evaluation measures, including accuracy, sensitivity, specificity, F1 score, and a receiver operating characteristic (ROC) curve. With only 10 features from the dataset, the proposed model χ 2 _RF achieved the highest accuracy of 94.59%. The proposed model χ 2 _RF improved the performance of the standard RF model by 5.5%. Moreover, the proposed model χ 2 _RF was compared with other state-of-the-art machine learning models. The experimental results show that the newly proposed decision support system outperforms the other machine learning systems using the same feature selection module ( χ 2 ). [ABSTRACT FROM AUTHOR]

This paper presents a deep neural network (DNN) based design optimization methodology for dual-axis microelectromechanical systems (MEMS) capacitive accelerometer. The proposed methodology considers the geometric design parameters and operating conditions of the MEMS accelerometer as input parameters and allows to analyze the effect of the individual design parameters on the output responses of the sensor using a single model. Moreover, a DNN-based model allows to simultaneously optimize the multiple output responses of the MEMS accelerometers in an efficient manner. The efficiency of the proposed DNN-based optimization model is compared with the design of the computer experiments (DACE) based multiresponse optimization methodology presented in the Literature, which showed a better performance in terms of two output performance metrics, i.e., mean absolute error (MAE) and root mean squared error (RMSE). [ABSTRACT FROM AUTHOR]

Municipal solid waste (MSW) management is an essential element of present-day society. The proper storage and disposal of solid waste is critical to public health, safety, and environmental performance. The direct recovery of MSW into useful energy is a critical task. In addition, the demand for conventional power supplies is high. As a strategy to solve these two problems, the technology to directly convert municipal solid waste into conventional energy to replace fossil fuels has been obtained. The hydrothermal carbonization (HTC) process is a thermochemical conversion process that utilizes heat to convert wet biomass feedstocks into hydrochar. Hydrochar with premium gasoline properties is used for fuel combustion for strength. The properties of fuel hydrochar, including C char (carbon content), HHV (higher heating value), and yield, are mainly based on the properties of the MSW. This study aimed to predict the properties of fuel hydrochar using a machine learning (ML) model. We employed an ensemble support vector machine (E-SVM) as the classifier, which was combined with the slime mode algorithm (SMA) for optimization and developed based on 281 data points. The model was primarily trained and tested on a fusion of three datasets: sewage sludge, leftovers, and cow dung. The proposed ESVM_SMA model achieved an excellent overall performance with an average R2 of 0.94 and RMSE of 2.62. [ABSTRACT FROM AUTHOR]

Background and objectives: Endodontic surgery has evolved over the last two decades. The use of state-of-the-art guided endodontic surgical procedures produces a predictable outcome in the healing of lesions of endodontic origin. The main objective of this review paper is to define and characterize guided surgical endodontics as well as its benefits and drawbacks by reviewing the most recent relevant scientific literature. Methods: A literature search was conducted using multiple databases comprising of MEDLINE (via PubMed), EMBASE, and Web of Science. The terms used for the search were 'guided endodontics', 'surgical endodontics', and 'endodontic microsurgery'. Results: In total, 1152 articles were obtained from the analysis of the databases. Unrelated articles from the available full text of 388 articles were excluded. A total of 45 studies were finally included in the review. Conclusions: Surgical-guided endodontics is a relatively new area of study that is still maturing. It has many applications such as root canal access and localization, microsurgical endodontics, endodontic retreatment, and glass fiber post removal. Additionally, it does not matter how experienced the operator is; the procedure can be completed for the patient in less time and provides greater accuracy and safety than conventional endodontics. [ABSTRACT FROM AUTHOR]

A protean career attitude is the most attractive and coping career adjustment attitude nowadays. Based on the social exchange theory, this study empirically analyses the association between protean career attitude and affective organisational commitment for Malaysian hotel industry employees. It also examines the COVID-19 situation's retrospective repercussions and career uncertainty. The study also investigates the moderating role of organisational career management on the relationship between protean career attitudes and affective organisational commitment. During the pandemic, a cross-sectional survey was given to 403 hotel managers working in four- or five-star hotels. The data were analysed using structural equation modelling in Smart-PLS. The results showed that self-directed and value-driven protean career attitudes undermine affective organisational commitment. Organisational career management significantly moderated the relationship between a protean career attitude and affective organisational commitment. In light of this, organisational career management is essential when dealing with protean careers. Lastly, the person's practical implications are significant. People should have a protean career attitude to deal with unpredictability, such as the COVID-19 epidemic and remain invincible over the long run. [ABSTRACT FROM AUTHOR]

Climacteric rise in the rate of respiration and ethylene production in harvested persimmon (Diospyros kaki) fruits leads to early onset of fruit tissue senescence. Therefore, this study was carried out to maintain the quality of stored persimmons by using edible coatings. For this purpose, Aloe vera gel was combined with food hydrocolloids, gum arabic or tragacanth gum, and applied on persimmon fruits that were stored for 20 days at 20 ± 1 °C and 80–85% RH. Biocomposite coatings, compared to control, remarkably reduced weight loss, decay incidence, respiration rate, ethylene production, electrolyte leakage, malondialdehyde, H2O2 and superoxide anion content in stored fruits. The use of composite coatings inhibited colour change by reducing the accumulation of total carotenoids, maintained higher antioxidative enzymes activity and suppressed the activity of cell wall degrading enzymes, resultantly preserving cell wall components. Composite coated fruits exhibited the least change in biochemical attributes and higher sensory scores in comparison with non-coated fruits at the end of the storage period. In conclusion, Aloe vera gel/gum arabic was the most effective coating treatment before storing persimmons. [ABSTRACT FROM AUTHOR]

This study was conducted in a rice-based cropping scheme to investigate the impact of green manuring on soil health, considering soil physicochemical properties and sustainable crop production. A field experiment was started on 2 November 2015 and completed on 15 April 2018 under a rice–berseem (Trifolium alexandrinum) cropping system in calcareous soil. Two green manuring patterns, rice–berseem and rice–wheat–sesbania (rostrata), were compared with a commonly practiced rice–wheat (Oryza sativa-Triticumaestivum) cropping pattern. Green manuring of the berseem crop (last cutting) along with 50, 75, and 100% of the recommended fertilizer doses of nitrogen, phosphorus, and potassium (NPK) were compared with recommended NPK fertilization along with control (no fertilizer). The plant growth parameters of rice, including plant height, tillers per plant at maturity, and yield data, were recorded at harvest. The pre-sowing soil analysis revealed that the experimental soil was low in salts, and the nutrient (NPK) status was very low. The results indicated that green manuring substantially enhanced the grain and straw yield of rice crops. Green manuring combined with 75% of recommended NPK produced the highest grain yield (5.83 t ha−1 in year III) compared to the other treatments. The soil analysis showed that the bulk density was reduced while soil porosity, organic carbon, and N, K, and P contents were significantly improved. From the results of this study, it is recommended that under calcareous soil conditions, the regular use of green manuring can significantly improve crop growth, yield, and physicochemical properties of soil and, therefore, should be adopted by farmers. [ABSTRACT FROM AUTHOR]

The Koper model is a vector field in which the differential equations describe the electrochemical oscillations appearing in diffusion processes. This work focuses on the understanding of the slow dynamics of a stochastic Koper model perturbed by stable Lévy noise. We establish the slow manifold for a stochastic Koper model with stable Lévy noise and verify exponential tracking properties. We also present two practical examples to demonstrate the analytical results with numerical simulations. [ABSTRACT FROM AUTHOR]

This manuscript presents a full duplex communication system for the Deaf and Mute (D-M) based on Machine Learning (ML). These individuals, who generally communicate through sign language, are an integral part of our society, and their contribution is vital. They face communication difficulties mainly because others, who generally do not know sign language, are unable to communicate with them. The work presents a solution to this problem through a system enabling the non-deaf and mute (ND-M) to communicate with the D-M individuals without the need to learn sign language. The system is low-cost, reliable, easy to use, and based on a commercial-off-the-shelf (COTS) Leap Motion Device (LMD). The hand gesture data of D-M individuals is acquired using an LMD device and processed using a Convolutional Neural Network (CNN) algorithm. A supervised ML algorithm completes the processing and converts the hand gesture data into speech. A new dataset for the ML-based algorithm is created and presented in this manuscript. This dataset includes three sign language datasets, i.e., American Sign Language (ASL), Pakistani Sign Language (PSL), and Spanish Sign Language (SSL). The proposed system automatically detects the sign language and converts it into an audio message for the ND-M. Similarities between the three sign languages are also explored, and further research can be carried out in order to help create more datasets, which can be a combination of multiple sign languages. The ND-M can communicate by recording their speech, which is then converted into text and hand gesture images. The system can be upgraded in the future to support more sign language datasets. The system also provides a training mode that can help D-M individuals improve their hand gestures and also understand how accurately the system is detecting these gestures. The proposed system has been validated through a series of experiments resulting in hand gesture detection accuracy exceeding 95%. [ABSTRACT FROM AUTHOR]

Objectives: This study assessed the reliability and validity of the DASS-21 self-reported measure in the context of COVID-19 on anxiety, stress, and depression. Through this Study, the psychological effect of COVID-19 on anxiety, tension, and depression amongst samples of students enrolled in 201 Malaysian private universities was assessed. Methods: The data were collected from university students through an online survey because of Malaysian Government Movement Control Order (MCO) restrictions. Two separate intervals were used for data collection (i.e., May and September 2020), as this period was associated with the pandemic. For scale validation, convergent, discriminant, and nomological validity criteria were used. Results: The outcome of a CFA model for DASS-21 yielded factor loading that is very significant. Therefore, the measure of the root means square error approximation (RMSEA) and the comparative fit index (CFI) are acceptable values that were produced, demonstrating a good fit for the data. Conclusions: This study was conducted in the Malaysian context to validate depression, anxiety, and stress among university students using the DASS-21 scale. Our findings support the reliability of using DASS-21 in the Malaysian cultural context. Lastly, we testified to the presence of depression, anxiety, and stress among university students through descriptive statistics and provided empirical evidence in this regard. Our results suggested that there was a significant presence of DASS among university students. [ABSTRACT FROM AUTHOR]

Rhizosphere soil ecosystems are represented by the diversity of different soil aggregate-size classes, such as large macroaggregates, small macroaggregates, mesoaggregates, and microaggregates. Though these aggregate-size classes represent distinct biological, chemical, and physical properties, little is known about their dynamics and relationships with belowground and aboveground plant traits. In this study, we examined the relationships of various soil aggregate-size classes and their organic carbon contents with many aboveground and belowground soybean plant traits. Our study revealed several novel and interesting relationships between soil structural properties and plant traits. Notably, small macroaggregates represented a major portion of the rhizosphere soil ecosystem of soybean plants while organic carbon contents decreased with decreasing size of soil aggregates. Only microaggregates showed a significant relationship with root architectural traits, such as length and surface area. Among all soil aggregate size classes, the abundance of small macroaggregates and the organic carbon contents of microaggregates were better correlated with plant traits. In general, organic carbon contents of different soil aggregate-size classes showed positive correlations with leaf trichome density (defense traits) and major macronutrients, such as root P, K, and S contents; while there were mostly negative correlations with some micronutrient (Ca, Mn, Zn, Cu, B, and Mg) contents of roots and shoots. However, the abundance of small macroaggregates mostly positively correlated with the mineral contents of plant roots and shoots. Collectively, the positive and negative correlations of organic carbon contents of different soil aggregate-size classes with trichomes (defense) and physiological traits (micro-mineral contents) suggest their significance in plant nutrition and defense. Though our results suggest the relationships of soil aggregate properties with aboveground and belowground traits, further research is needed to discern the role of soil structural traits in mediating plant growth, development, defense, and physiology. [ABSTRACT FROM AUTHOR]

Mixed metallic oxides are getting increasing attention as novel electrode materials for energy conversion devices. However, low mixed ionic-electronic conductivity and high operating temperature hamper the practical applications of these devices. This study reports an effective strategy to improve the conductivity and performance of the fuel cell at low temperature by partially incorporating graphene in the Li0.1Cu0.2Zn0.7-oxide (LCZ) composite. The proposed cathode material is synthesized via the cost effective conventional solid-state route. Graphene incorporated LCZ shows excellent performance, which is attributed to the favorable charge transport paths offering low area-specific resistance. An X-ray diffractometer (XRD) and scanning electron microscope (SEM) are employed for microstructural and surface morphological analyses, respectively. Electrical conductivities of all the materials are determined by the DC four probe method, and interestingly, LCZ-1.5% graphene exhibits an excellent conductivity of 3.5 S/cm in air atmosphere at a temperature of 450 °C with a minimum value of 0.057 Ωcm2 area-specific resistance (ASR) that demonstrates significantly good performance. Moreover, the three-layer fuel cell device is fabricated using sodium carbonated Sm0.2Ce0.8O (NSDC) as an electrolyte, which can operate at low temperatures exhibiting open circuit voltage 0.95 V and shows a peak power density, i.e., 267.5 mW/cm2 with hydrogen as the fuel. [ABSTRACT FROM AUTHOR]