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Climate vulnerability in the Indian region becomes a major concern due to the frequent hydro-meteorological extreme events throughout the year. The number of extreme rainfall events (ERE) associated with monsoon, thunderstorms and short-spanned local cloudbursts which are mainly ranges from a few hours to a few days, has increased in recent decades over the subcontinent. There is a need to quantify these high impact weather events and to understand the spatio-temporal dynamics and associated physical processes for the real time prediction for pro-active disaster management. This review paper highlights the various research works carried out and suggests the need of observation, modelling efforts for simulation/prediction of the EREs in the continental India. The works starting from cloudburst in the Himalayan region (Leh, Uttarakhand, Himachal Pradesh) to the heavy rainfall in Kerala, Bangalore, Mumbai, Chennai etc. as presented by earlier researchers are being reported in this compilation. The ERE intensity and frequency distribution in India are being analyzed using IMD gridded rainfall observation for 72 years. The analysis indicates the increasing trend in the intensity of daily rainfall during the ERE in the subcontinent. The current state of research in the multi-scale study of the ERE as well as their future changes based on climate model outputs are reviewed and the challenges are also reviewed. Evaluation of the capability of the state of art meso scale models like Weather Research and Forecasting (WRF) model for the simulation of such events at high resolution using the high-performance computing and the assimilation techniques are also emphasized in the recent research in this field. Along with the impact of large-scale physical processes like El Nino Southern Oscillations (ENSO), also the sensitivity of the urbanization, topography in the simulation of the convective meso-scale events is being presented and which indicated the down-scaled data are the best tool for the ERE research. This review will surely help the interdisciplinary researchers for the extreme analysis of the high impact hydro-meteorological disaster events.

The country wide lockdown implemented during 27th April to 14thJune 2021 in order to prevent the spread of COVID-19 during the second wave in India. Effect of the restricted lockdown resulted in improved air quality. This study focuses on analyzing the spatio-temporal distribution analysis of major air pollutant concentration over Bangalore city in India. The inverse distance weighting (IDW) method is implemented for the spatial analysis in order to quantify the distribution of the pollutant concentrations at each location in the Urban city of Bangalore. The research considers the distinct periods of pre-lockdown and lockdown during the second wave of COVID-19 pandemic in 2021 to investigate the impact of reduced human activities on air quality over the city. The study mainly utilizes the air pollution data collected from Central Pollution Control Board (CPCB) monitoring stations across Bangalore, including measurements of pollutants such as PM2.5, PM10, O3, NO2, SO2, and CO. The IDW method is implemented to create the high-resolution pollution concentration maps for both the pre-lockdown and lockdown periods. This spatial distribution provides valuable insights into the variations in the pollution levels though out the Bangalore city. The comparative analysis of the concentration maps reveals significant changes in air pollution levels between the two periods; similarly, the temporal weekly average analysis also witnessed negative anomalies during the lockdown weeks. The results indicate substantial reductions in pollutant concentrations during the second wave COVID-19 lockdown, attributed to decreased vehicular emissions, industrial activities, and construction operations. The pre-lockdown period serves as a baseline for assessing the improvements in air quality during the lockdown. The spatio-temporal modeling approach enhances our understanding of the distribution patterns of air pollutants across the Bangalore metropolitan city. The findings underscore the potential benefits of implementing sustainable strategies to maintain improved air quality even after the pandemic subsides.

We propose fully distributed multi-group multicast precoding designs for cell-free massive multiple-input multiple-output (MIMO) systems with modest training overhead. We target the minimization of the sum of the maximum mean squared errors (MSEs) over the multicast groups, which is then approximated with a weighted sum MSE minimization to simplify the computation and signaling. To design the joint network-wide multi-group multicast precoders at the base stations (BSs) and the combiners at the user equipments (UEs) in a fully distributed fashion, we adopt an iterative bi-directional training scheme with UE- and/or group-specific precoded uplink pilots and group-specific precoded downlink pilots. To this end, we introduce a new group-specific over-the-air uplink training resource that entirely eliminates the need for backhaul signaling for the channel state information (CSI) exchange. The precoders are optimized locally at each BS by means of either best-response or gradient-based updates, and the convergence of the two approaches is analyzed with respect to the centralized implementation with perfect CSI. Finally, numerical results show that the proposed distributed methods greatly outperform conventional cell-free massive MIMO precoding designs that rely solely on local CSI.

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This article proposes a novel real-time fault diagnosis approach for open-switch faults of three-phase pulsewidth modulated voltage source inverter induction motor drive system. The diagnosis method relies only on the observed current via processing the extracted dc components and current absolutes using dynamic resonant filters (DRFs). By employing the ratio between the extracted dc components along with the dc component of absolute line currents and absolute line currents sum, 33 different types of faults can be detected and discriminated effectively. A fuzzy logic-based subsystem is dedicated to accurate reporting of different fault scenarios and manipulating the detection delay caused by the extracted dc component oscillation introduced by the DRFs. The proposed approach is parameter-free, fast, reluctant to noise presence, applicable for a wide range of switching frequencies, and independent of load-level or transient variations. Besides, it is suitable for any type of control technique either open or closed loop without any excessive sensors. Extensive experimental and time-domain investigations are examined, and the obtained results reveal the effectiveness of the proposed fault detection method.

This paper addresses the vulnerability of vehicular ad hoc networks (VANETs) to malicious attacks, specifically focusing on position falsification attacks. Detecting misbehaving vehicles in VANETs is challenging due to the dynamic nature of the network topology and vehicle mobility. The paper considers five types (constant attack, constant offset attack, random attack, random offset attack, and eventually stop attack) of position falsification attacks with varying traffic and attack densities, considered the most severe attacks in VANETs. To improve the detection of these attacks, a novel travel distance feature and an enhanced two-stage detection approach are proposed for classifying position falsification attacks in VANETs. The approach involves deploying the misbehavior detection system within roadside units (RSUs) by offloading computational work from vehicles (onboard units, or OBUs) to RSUs. The performance of the proposed approach was evaluated against different classifiers, including a wide range of paradigms (KNN, Decision Tree, and Random Forest), using the VeReMi dataset. Experimental results indicate that the proposed method based on Random Forest achieved an accuracy of 99.9% and an F1-Score of 99.9%, which are better not only than those achieved by KNN and Decision Tree but also than the most recent approaches in the literature survey.

The Japan Astrometry Satellite Mission for INfrared Exploration (JASMINE) is a planned M-class science space mission by the Institute of Space and Astronautical Science, the Japan Aerospace Exploration Agency. JASMINE has two main science goals. One is Galactic archaeology with a Galactic Center survey, which aims to reveal the Milky Way’s central core structure and formation history from Gaia-level (∼25 ${\mu} $as) astrometry in the near-infrared (NIR) Hwband (1.0–1.6 ${\mu} $m). The other is an exoplanet survey, which aims to discover transiting Earth-like exoplanets in the habitable zone from NIR time-series photometry of M dwarfs when the Galactic Center is not accessible. We introduce the mission, review many science objectives, and present the instrument concept. JASMINE will be the first dedicated NIR astrometry space mission and provide precise astrometric information on the stars in the Galactic Center, taking advantage of the significantly lower extinction in the NIR. The precise astrometry is obtained by taking many short-exposure images. Hence, the JASMINE Galactic Center survey data will be valuable for studies of exoplanet transits, asteroseismology, variable stars, and microlensing studies, including discovery of (intermediate-mass) black holes. We highlight a swath of such potential science, and also describe synergies with other missions.

The burden of chronic kidney disease and associated risk of kidney failure are increasing in Africa. The management of people with chronic kidney disease is fraught with numerous challenges because of limitations in health systems and infrastructures for care delivery. From the third iteration of the International Society of Nephrology Global Kidney Health Atlas, we describe the status of kidney care in the ISN Africa region using the World Health Organization building blocks for health systems. We identified limited government health spending, which in turn led to increased out-of-pocket costs for people with kidney disease at the point of service delivery. The health care workforce across Africa was suboptimal and further challenged by the exodus of trained health care workers out of the continent. Medical products, technologies, and services for the management of people with nondialysis chronic kidney disease and for kidney replacement therapy were scarce due to limitations in health infrastructure, which was inequitably distributed. There were few kidney registries and advocacy groups championing kidney disease management in Africa compared with the rest of the world. Strategies for ensuring improved kidney care in Africa include focusing on chronic kidney disease prevention and early detection, improving the effectiveness of the available health care workforce (e.g., multidisciplinary teams, task substitution, and telemedicine), augmenting kidney care financing, providing quality, up-to-date health information data, and improving the accessibility, affordability, and delivery of quality treatment (kidney replacement therapy or conservative kidney management) for all people living with kidney failure.

Tumor-agnostic, or histology-agnostic, cancer therapy marks a groundbreaking evolution in the realm of precision oncology. In stark contrast to conventional cancer treatments that categorize malignancies based on their tissue of origin (eg, breast, lung, renal cell, etc), tumor-agnostic therapies transcend histologic boundaries, honing in on the genetic and molecular attributes of tumors, regardless of their location. This article offers a comprehensive review of the current landscape of tissue-agnostic cancer therapies and provides clinical insights to empower surgical oncologists with a deeper understanding of these innovative therapeutic approaches.

AbstractInfrastructure Project (IP) construction in Egypt is saddled with copious risks emanating from the manque of risk knowledge, driving these projects to enigmatic failure concerning construction cost. The paper’s novel contribution is to acquaint with an Infrastructure Neural Risk Model (INRM) approach that colossally spotlights the substantial risk factors confronting Egypt’s IP construction apropos to Probability of Occurrence (POO) and Impact on Cost (IOC). Hundred and fifty-seven risk factors were compiled from the literature under three risk categories and reduced to 10, portraying the critical risk influencing the IP construction costs inconsolably. The paper’s main merits include identifying the puritanical risk factors impacting the IP construction costs and bestowing contractors with a paradigm for anticipating risk factors POO and their respective IOC among the IP. Five chronological steps constitute the developed approach inaugurating with (1) conducting a thorough analysis of risk management studies in the IP, (2) adopting a Risk Breakdown Structure (RBS), (3) implementing a checklist analysis to recall the substantial IP risks, (4) constituting a questionnaire survey to interrogate the forthcoming risk factors inferred from comprehensive prior research, and (5) developing INRM paradigm to anticipate the prospective risk factors post-mitigation impact on the construction cost. According to the findings, the coefficient of determination (R2) of the developed INRM unearthed the best INRM outcomes, and the optimal architecture in training and test datasets is 0.872 and 0.777, respectively.

A comparative study on fatigue life of deep groove radial ball bearings possessing conventional and micro-grooved outer races by experimental investigation is discussed in this paper. This research has been carried out to explore and understand the fatigue life of a micro-grooved ball bearing (MGB) with a circumferential micro-groove on outer race by conducting long duration tests under dry condition (i.e. wiping out the applied grease from the races by using tissue paper and lint free cloth) and compare the results with same size SKF BB1B 420206 conventional bearing (CB) at the identical operating parameters. Frictional torque (FT), bulk temperature, and overall vibration level of the test bearings (CB and MGB) at the maximum load zone have been measured at 800 N (pH= 1.7 GPa) radial load and 1800 rpm (4.2 m/s) for the entire duration (7.8 million revolutions) of the experiments. Comparison of the surface conditions of the bearing components by means of the captured scanning electron microscope (SEM) images, roundness and deviation in profile of the races measured by co-ordinate measuring machine (CMM), and photographs captured before and after the experiment indicates lesser damage of the MGB components as compared to the conventional cases. Moreover, the initiation of fatigue failure in CBs is faster as compared to the MGBs for the same test condition, indicating longer bearing life in MGBs as compared to the CBs.

Tissue-agnostic approvals, based on basket trials of multiple tumor histology, permit biomarker-driven targeting of specific shared molecular alterations, regardless of the site of origin of the tumor.Tissue-agnostic targets, where efficacy data on primary brain tumors is available, include BRAF V600Emutations, neurotrophic tyrosine receptor kinase fusions, high tumor mutational burden, and microsatellite instability (MSI), or mismatch repair deficiency.Future targets for tissue-agnostic approvals where utility in primary brain tumors remains to be demonstrated include FGFR, ERBB2/HER2, KRASG12C, and TP53Y220C.Multicenter prospective registries and real-world evidence are needed to reaffirm the utility of these therapeutics in clinical practice.

Objective: Systemic sclerosis is an autoimmune disease characterized by fibrosis of the skin and internal organs including the lung. Mucosal-associated invariant T cells are innate-like T lymphocytes able to produce various cytokines and cytotoxic mediators such as granzyme B. A large body of evidence supports a role of mucosal-associated invariant T cells in autoimmune disease but more recent reports suggest also a potential role in fibrotic conditions. Therefore, we herein addressed the question as whether mucosal-associated invariant T cells may have an altered profile in systemic sclerosis.Methods: Mucosal-associated invariant T cell frequency was analyzed by flow cytometry, using fresh peripheral blood from 74 consecutive systemic sclerosis patients who were compared to 44 healthy donors. In addition, in-depth mucosal-associated invariant T cell phenotype and function were analyzed in unselected 29 women with systemic sclerosis who were compared to 23 healthy women donors.Results: Proportion of circulating mucosal-associated invariant T cells was significantly reduced by 68% in systemic sclerosis compared to healthy donors (0.78% in systemic sclerosis vs 2.5%, p< 0.0001). Within systemic sclerosis subsets, mucosal-associated invariant T cells were reduced in patients with interstitial lung disease (systemic sclerosis-interstitial lung disease) (0.56% vs 0.96% in patients without interstitial lung disease, p= 0.04). Moreover, in systemic sclerosis patients, mucosal-associated invariant T cells displayed an activated phenotype indicated by markedly increased CD69+mucosal-associated invariant T cell frequency (20% mucosal-associated invariant T cell CD69+compared to 9.4% in healthy donors, p= 0.0014). Interestingly, mucosal-associated invariant T cells from systemic sclerosis-interstitial lung disease patients had a more pronounced altered phenotype compared to systemic sclerosis without interstitial lung disease with a correlation between mucosal-associated invariant T cells expressing CCR6+and mucosal-associated invariant T cell frequency (r = 0.8, p= 0.006).Conclusion: Circulating mucosal-associated invariant T cells were reduced and exhibited an activated phenotype in systemic sclerosis patients. This peripheral mucosal-associated invariant T cell deficiency may be related to enhanced apoptosis and/or homing in inflamed tissue, particularly in systemic sclerosis-interstitial lung disease patients.

In the present investigation, we have utilized phospholipid fatty acid (PLFA) technique to study the microbial communities in the gut region of Apis mellifera.A total of six microbial groups belonging to gram-positive bacteria, gram-negative bacteria, anaerobes, actinomycetes, microeukaryotes, and fungi were recorded from the Indian population of A. mellifera. The alpha diversity indices, evince that the hindgut of the hive bees is the favoured niche for a diverse microbial community. The gut microbiota of hive and forager bees exhibits a positive correlation with actinomycetes, gram-positive bacteria, and fungi (r= 0.73; r= 0.46; r= 0.21). Furthermore, the hindgut and foregut regions of hive bees, as well as the foregut of foragers, display a positive correlation (r= 0.63; r= 0.57; r= 0.57) with all microbial communities examined. Results of this study reveal the existence of distinct niches for diverse gut microbial groups in different gut regions of A. mellifera.

Wind power in tropical countries like India has great potential as a major source of green energy. However, in order to do proper energy provisioning, there is a need to forecast and estimate the wind speed at windmill locations with an actionable lead-time. Forecasting wind speed at station level is a big challenge using dynamical models as it gives only macro level information. Therefore, use of statistical models is preferably adopted for this purpose. With the recent phenomenal growth of applications in artificial intelligence (AI), it is also possible to use data-driven models based on AI, especially deep learning for short-term forecasting of wind speed. In this paper, we have proposed a novel ensemble forecasting methodology using the long short-term memory (LSTM) model, which is a deep learning approach for time series data analysis. The capability of this approach has been demonstrated using wind speed data obtained from two meteorological stations located at New Delhi in North India and at Bengaluru in South India. We have used the ensemble methodology in two different modes; one is the averaging pooling and other is by using a hierarchical LSTM. The simulations using these models have been validated against the true observations at station scale. The ensemble forecasting method has shown promising results for 3-h early wind speed prediction at both the locations. The results are also compared with two classical statistical methods namely autoregressive and persistence models and two state-of-the-art data-driven models namely support vector machine (SVM) and extreme learning machine (ELM). The capability of the proposed method is demonstrated through various error matrices and found to have better performance. We believe that the proposed method has the potential to improve the short-term wind speed prediction capability at station level.

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The sorption of some essential ions by economical and effective methods is crucial in development and technology. The sorption of Cs(I), Co(II), and Ni(II) from aqueous solutions using carboxymethyl cellulose/clay (CMC/clay) sorbent synthesized by precipitation method was studied by batch technique. Different analytical tools like SEM, XRD, FTIR, DTA, and TGA analysis were utilized to find morphology, structure, functional groups, and thermal analysis for CMC/clay. The adsorption process was performed by changing different adsorption factors such as pH, shaking time, initial Cs(I), Co(II), and Ni(II) concentrations, and temperature to get maximum removal of the studied cations. The elements removal process followed pseudo-second-order with the values of regression factor (R2) found as 0.988, 0.995, and 0.996 for Cs(I), Co(II), and Ni(II), respectively. The equilibrium data fitted well to the Langmuir isotherm. The monolayer capacity values were found to be 25.9, 23.7, and 18.5 mg/g for Cs(I), Co(II), and Ni(II), respectively. The thermodynamic functions reflect an endothermic and spontaneous sorption process. Desorption of Cs(I), Co(II), and Ni(II) from the loaded CMC/clay was done using different eluents, and the best eluant was 0.5M of HCl. The data revealed that CMC/clay sorbent is suitable for recovering Cs(I), Co(II), and Ni(II) from aqueous solutions. The real sample application proved that CMC/clay is an excellent sorbent for the capture of Ni(II) from industrial wastewater.

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The development of new catalytic protocols for clean and COx-free hydrogen generation from fundamental feedstocks is always interesting and challenging. Herein, we disclose nickel-catalyzed dihydrogen generation from a mixture of paraformaldehyde–methanol under base-free and activator-free conditions. The dihydrogen generation from this redox combination under neutral, oxidative coupling conditions has been integrated with the hydrogen transfer reactions such as chemo- and stereoselective hydrogenation of alkynes in a tandem manner. This unprecedented strategy provides diverse highly stereoselective olefins with excellent tolerance of reducible functional groups such as ether, silyl ether, aldehyde, keto, ester, nitrile, halides including bromo and iodo groups, and heteroarenes. Additionally, we demonstrated catalytic stereo-interconversion of alkenes under benign conditions. The affordable gram-scale synthesis of some important pharmaceutical bioactive molecules has further enhanced their synthetic value.

Surface electromyography (sEMG)-based gesture recognition is a key technology in the field of human–computer interaction. However, existing gesture recognition methods face challenges in effectively integrating discriminative temporal feature representations from sEMG signals. In this paper, we propose a deep learning framework named TFN-FICFM comprises a Temporal Fusion Network (TFN) and Fuzzy Integral-Based Classifier Fusion method (FICFM) to improve the accuracy and robustness of gesture recognition. Firstly, we design a TFN module, which utilizes an attention-based recurrent multi-scale convolutional module to acquire multi-level temporal feature representations and achieves deep fusion of temporal features through a feature pyramid module. Secondly, the deep-fused temporal features are utilized to generate multiple sets of gesture category prediction confidences through a feedback loop. Finally, we employ FICFM to perform fuzzy fusion on prediction confidences, resulting in the ultimate decision. This study conducts extensive comparisons and ablation studies using the publicly available datasets Ninapro DB2 and DB5. Results demonstrate that the TFN-FICFM model outperforms state-of-the-art methods in classification performance. This research can serve as a benchmark for sEMG-based gesture recognition and related deep learning modeling.

Lithium–sulfur batteries (LSBs) show promise for achieving a high energy density of 500 W h kg−1, despite challenges such as poor cycle life and low energy efficiency due to sluggish redox kinetics of lithium polysulfides (LiPSs) and sulfur's electronic insulating nature. We present a novel 2D Ti3C2Mxene on a 2D graphitic carbon nitride (g-C3N4) heterostructure designed to enhance LiPS conversion kinetics and adsorption capacity. In a pouch cell configuration with lean electrolyte conditions (∼5 μL mg−1), the g-C3N4-Mx/S cathode exhibited excellent rate performance, delivering ∼1061 mA h g−1at C/8 and retaining ∼773 mA h g−1after 190 cycles with a Coulombic efficiency (CE) of 92.7%. The battery maintained a discharge capacity of 680 mA h g−1even at 1.25 C. It operated reliably at an elevated sulfur loading of 5.9 mg cm−2, with an initial discharge capacity of ∼900 mA h g−1and a sustained CE of over 83% throughout 190 cycles. Postmortem XPS and EIS analyses elucidated charge–discharge cycle-induced changes, highlighting the potential of this heterostructured cathode for commercial garnet LSB development.

In the current study, the epoxy material was mixed with 10%, and 30% weight percent carbon material as filler in different thicknesses (1 cm, 1.5 cm, and 2 cm). Transmission electron microscope (TEM) measurements showed the average size of the nano-carbon was 20 nm with a standard deviation of 5 nm. The morphology of samples was examined using scanning electron microscopy (SEM), which showed the flatness of the epoxy surface, and when the content of carbon increases, the connection between the epoxy array and carbon increases. The compression test indicates the effect of nano-size on enhancing the mechanical properties of the studied samples. To survey the shielding properties of the epoxy/carbon composites using gamma-rays emitted from Am-241, Ba-133, Cs-137, Co-60, and Eu-152 sources, which covered a wide range of energies from 0.059 up to 1.408 MeV, the gamma intensity was measured using the NaI (Tl) detector. The linear and mass attenuation coefficients were calculated by obtaining the area under each peak of the energy spectrum observed from Genie 2000 software in the presence and absence of the sample. The experimental results obtained were compared theoretically with XCOM software. The comparison examined the validity of experimental results where the relative division rate ranged between 0.02 and 2%. Also, the measurement of the relative division rate between linear attenuation coefficients of micro- and nano-composites was found to range from 0.9 to 21% The other shielding parameters are calculated at the same range of energy, such as a half-value layer (HVL), mean free path (MFP), tenth-value layer (TVL), effective atomic number (Zeff), and the buildup factors (EBF and EABF). The data revealed a consistent reduction in the particle size of the shielding material across various weight percentages, resulting in enhanced radiation shielding capabilities. The sample that contains 30% nano-carbon has the lowest values of TVL (29.4 cm) and HVL (8.85 cm); moreover, it has the highest value of the linear attenuation coefficient (LAC), which makes it the best in its ability to attenuate radiation.

The main objective of the current trial was to evaluate the beneficial influences of Origanum vulgareleaf extract (OVLE) on growth, some blood indices, redox status, and the expression of some growth and immune-related genes. Three thousand seven hundred and eighty Nile tilapia fingerlings were allocated to six equal groups. The first group served as a control and received a basal diet, while the other five groups were fed diets containing graded amounts of OVLE (0.1, 0.2, 0.3, 0.4, and 0.5%, respectively) and defined as OVLE0.1, OVLE0.2, OVLE0.3, OVLE0.4,and OVLE0.5for 98 days. Growth performance and feed efficiency parameters were significantly improved in OVLE0.4and OVLE0.5groups compared with the control group. The hematological indices, specifically hemoglobin, red blood cell, and white blood cell count were significantly increased in the fish group fed a 4% OVLE-supplemented diet compared to other groups. Fish fed higher dietary inclusion levels of OVLE significantly increased total protein and albumin concentrations compared to other experimental groups. The OVLE0.4and OVLE0.5supplemented groups promoted innate immune response and phagocytic activity and stimulated the lipase enzyme activity of Nile tilapia. All estimated redox markers were significantly improved in the fish groups that received 4 or 5% OVLE compared with the control and other OVLE groups. The fish groups fed higher levels of OVLE significantly upregulated the expression of GH, IL-8, and IL-1βgenes and downregulated the expression of the Hsp70gene. Furthermore, the intestinal histological features demonstrated normal structure in all OVLE-administered fish groups, with improved intestinal villus length compared to the untreated group. Finally, it could be concluded that including OVLE in the Nile tilapia diet at higher levels (0.4 up to 0.5%) could improve performance and general health status.

The aim of this article is to introduce the concept of centrally-extended Jordan epimorphisms and proving that if Ris a non-commutative prime ring (∗-ring) of characteristic not two, and Gis a CE-Jordan epimorphism such that [G(x), x] ∈ Z(R) ([G(x), x∗] ∈ Z(R)) for all x∈ R, then Ris an order in a central simple algebra of dimension at most 4 over its center or there is an element λin the extended centroid of Rsuch that G(x) = λx(G(x) = λx∗) for all x∈ R.

In the distributed sensor network, it is challenging to secure communication while simultaneously being aware of the intermittent failure situation of a sensor node during the connection. The existing methods rely on KNN with statistical methods and iterative to identify error-free communication for the random behaviour of the sensor node. This research developed a KNN-based method for predicting whether a transmission would be faulted or fault-free and the statistics of sensor received data over a specific time interval, time period, and amount of time measures and compares the distance statistics of the sensor node at a predetermined, specific tolerance level. Moreover, in the simulation study, the entire network is based on the sending and receiving data status in a distributed WSN for real-time measurement with 100% data accuracy, a lower FPR, and a 0% FAR. All the experimental results found the statistical distance from a problematic cluster node exceeds 30%.

We consider a cell-free massive multiple-input multiple-output system with multi-antenna access points (APs) and user equipments (UEs), where the UEs can be served in both the downlink (DL) and uplink (UL) within a resource block. We tackle the combined optimization of the DL precoders and combiners at the APs and DL UEs, respectively, together with the UL combiners and precoders at the APs and UL UEs, respectively. To this end, we propose distributed beamforming designs enabled by iterative bi-directional training (IBT) and based on the minimum mean squared error criterion. To reduce the IBT overhead and thus enhance the effective DL and UL rates, we carry out the distributed beamforming design by assuming that all the UEs are served solely in the DL and then utilize the obtained beamformers for the DL and UL data transmissions after proper scaling. Numerical results show the superiority of the proposed combined DL-UL distributed beamforming design over separate DL and UL designs, especially with short resource blocks.

Using biomass materials for activated carbon preparation through a simple and expedient method is a highly appropriate approach for sustainable and green development. Here, for the first time the waste neem (Azadirachta Indica) leaf was used for the development of activated carbon in the microwave process assisted with K2CO3as an activating agent, and its performance was checked for methylene blue dye (MB) removal. The neem leaves activated carbon (NLAC) was analyzed with sophisticated instrument, where the SEM, XRD and pHIEPresults showed that NLAC surface has highly active pores, is crystalline in nature and has a pHIEPvalue of 6.9. The FTIR data indicated that a number of active functional groups (–OH, –COOH, etc.) were present on the NLAC surface. The BET data presented that material has a good surface area of 50.69 m2/g and a pore volume of 0.014 cm3/g. More than 96% of MB dye was removed in the obtained environments of 10 mg/L MB dye concentration, pH 7, contact time 100 min, adsorbent dose of 70 mg and temperature 80 °C, respectively. The removal data were suitably followed by Langmuir isotherm model (R2= 0.96) having an uptake capacity of 132.52 mg/g. Kinetics data for MB removal were matched with the pseudo-second-order model with a R2of 0.96. The adsorption process was spontaneous in nature, where ΔHand ΔSvalues were 0.029 kJ/mol and 0.095 kJ/Kmol. The adsorption mechanism was followed by three types of interaction, i.e., electrostatic attraction, hydrogen bonding and pi–pi interaction, respectively.

AbstractThe Mega Housing Projects (MHP) construction in Egypt is saddled with numerous risks that emerged from the risk knowledge dearth, steering these projects to elusive failure apropos to completion time and construction cost. The research's novel contribution is a risk assessment Structural Equation Modeling (SEM) approach that spotlights the critical risk factors confronting MHP in Egypt concerning Probability of Occurrence (POO), Impact on Time (IOT), and Impact on Cost (IOC), from the contractors perspective. Forty-three risk factors were amassed from the literature and lessened to 25, delineating the significant risks impacting the MHP schedule and budget resentfully. The primary paper's objectives are determining the captious risk factors and ranking their relative importance weights. A questionnaire survey was conducted, and distinctive construction professionals solicited feedback. Quantitative data obtained from 103 valid responses were analysed using SEM, and the measurement and structural models were validated. Data analysis revealed that the project management risks procure the prime relative importance weight concerning POO, IOT, and IOC at 32.09%, 26.04%, and 32.93% mutually. Findings will equip contractors with a model to pinpoint and rank the censorious risk factors impacting the MHP to prioritise allotting budgetary scenarios among risks to vanquish cost and time overruns.

The past few years have witnessed significant progress in emerging disease detection techniques for accurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focus on image processing techniques using machine learning (ML) and deep learning (DL) models related to multi-scale rice diseases. Furthermore, we summarize applications of different detection techniques, including genomic, physiological, and biochemical approaches. In addition, we also present the state-of-the-art in contemporary optical sensing applications of pathogen–plant interaction phenotypes. This review serves as a valuable resource for researchers seeking effective solutions to address the challenges of high-throughput data and model recognition for early detection of issues affecting rice crops through ML and DL models.

ABSTRACTConvergence excess esotropia is a condition characterized by an esotropia which is greater for near fixation than for distance fixation after full hypermetropic correction with a single focus lens. Convergence excess esotropia may be classified according to the AC/A ratio into two subtypes: accommodative type and non-accommodative type. Bifocal glasses are a suitable option for the management of patients with a high AC/A ratio and for the hypoaccommodative type. However, the overall success rate with bifocals is still low even in selected patients. Surgery is often eventually needed for most patients with convergence excess esotropia. Surgical options that do not directly address the variability of the angle of deviation entail medial rectus recession with the target angle based on the distance deviation, the near deviation, an augmented formula based on an intermediate angle, or on a prism adaptation test. Surgical options that directly address the variability of the angle include partial myotomy, medial rectus muscle posterior scleral fixation with or without recession, pulley fixation, slanting recession, Y-splitting, or combined recession-resection of the medial rectus muscle. The review article summarizes the surgical outcome of these strategies and suggests an algorithm for the management of patients with convergence excess esotropia.