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Quackery in dentistry has been a problem for decades. It arises when there are inadequate numbers of competent and trained practitioners or when their charges appear prohibitive to a segment of the population. Poorly educated people often become gullible prey to quacks who perform dental treatment, which is often harmful to patients. It is time to take measures to stop such unethical practices. The purpose of this paper is to highlight the various causes of growing dental quackery in India, the possible measures to prevent it and the malpractices being carried out by quacks. It will also propose a classification of oral healthcare providers in India.

High vehicle mobility, changing vehicle density and dynamic inter-vehicle spacing are all important issues in the VANET environment. As a result, a better routing protocol improves VANET overall performance by permitting frequent service availability. Therefore, an ensemble-based machine-learning technique is used to forecast VANET mobility. Effective routing based on a hybrid metaheuristic algorithm combined with Ensemble Learning yields significantly improved results. Based on information collected from the Road Side Unit (RSU) or the Base Station, a hybrid metaheuristic (Seagull optimization and Artificial Fish Swarm Optimization) method is used to estimate (BS). The suggested approach incorporates an ensemble machine learning and hybrid metaheuristic method to reduce the latency. The current model's execution is calculated using a variety of Machine Learning techniques, including SVM, Nave Bayes, ANN, and Decision Tree. As a result, the performance of machine learning algorithms may be studied and used to achieve the best results. Comparative analysis between the proposed method (HFSA-VANET) and (CRSM-VANET was done on different performance parameters like throughput, delay, drop, network lifetime, and energy consumption to assess system performance on two factors Speed and Nodes. The HFSA-VANET method shows an overall drop in the delay of 33% and a decrease in the energy consumption of 81% and an increase of 8% in the throughput as compared with the CRSM-VANET method at 80 node. The proposed method that is HFSA-VANET has been implemented in the MATLAB and NS2 environment.

Urbanization is expected to hold about 50% of the world population by 2050 and there will be stress on available resources including food and freshwater. Further, inefficient utilization of irrigation water possesses additional challenges and it is predicted that by 2050, India will face shortage of freshwater resources. Urban agriculture and Precision Agriculture are the possible solutions in smart cities for the predicted challenges providing food security and safety, sustainable life in cities. Internet of Things (IoT) and Machine Learning (ML) are the possible solutions to smart cities' farming. Agricultural activities such as irrigation of plants, rain and drought conditions monitoring supported by IoT and ML succour farmers to righteous decision on agriculture activities. A detailed study is carried out on the role, opportunities, and different aspects of smart cities, urban farming, communication technologies, IoT and ML with respect to agriculture. The article presents the design of intelligent irrigation system based on soil and weather conditions. The soil and weather parameters are selected through various research articles in Agriculture 4.0 and ML. The article also juxtapositions the designed weather station with the various patents developed. The system developed in this article provides a cost‐effective and state‐of‐the‐art solution to local weather monitoring.

We develop a polynomial time algorithm for the single-source all destinations shortest paths problem for interval temporal graphs (ITGs). While a polynomial time algorithm for this problem is known for contact sequence temporal graphs (CSGs), no such prior algorithm is known for ITGs. We benchmark our ITG algorithm against that for CSGs using datasets that can be solved using either algorithm. Using synthetic datasets, experimentally, we show that our algorithm for ITGs obtains a speedup of up to 32.5 relative to the state-of-the-art algorithm for CSGs.

The paper describes flow control techniques viz. vane-type vortex generator (VG) array and rear-spoiler on its trunk (boot) side used to reduce drag of a passenger car. The experimental and computational studies were carried out and different cases and combinations were analyzed for the car model by varying incoming airflow angle and spoiler angle and orientations of VG array to find out the optimum conditions for which drag coefficient is found minimum. Shear stress transport (SST) k-w turbulence model is found suitable in predicting the multi-scale rear-wake vortices of the car geometry. It is found that the crossflow increases the drag coefficient, which can however be reduced effectively if both VG array and rear-spoiler are used. Parametric analysis shows that counter-rotating VG array is found useful in reducing drag (around 23 %) as it promotes better flow mixing at its downstream, which is helpful in avoiding flow separation. The finding is also supported by the flow visualization study. It is also found that saving up to 11.5 % in the fuel consumption can be achieved by reducing drag using these techniques. The wake analysis and turbulent kinetic energy plots indicated that the counterrotating VG array while used with a rear spoiler parallel to the flow reduced drag considerably.

The min-wait foremost, min-hop foremost and min-cost foremost paths and walks problems in interval temporal graphs are considered. We prove that finding min-wait foremost and min-cost foremost walks and paths in interval temporal graphs is NP-hard. We develop a polynomial time algorithm for the single-source all-destinations min-hop foremost paths problem and a pseudopolynomial time algorithm for the single-source all-destinations min-wait foremost walks problem in interval temporal graphs. We benchmark our algorithms against algorithms presented by Bentert et al. for contact sequence graphs and show, experimentally, that our algorithms perform up to 207.5 times faster for finding min-hop foremost paths and up to 23.3 times faster for finding min-wait foremost walks.

Stealth wearable wireless devices are gaining much attention in the personal security and fashion designing industry. A multiple-input multiple-output wideband circularly polarized antenna wearable on a dress (textile-related), which is immune to bending, is described in this paper, where a wearable on fabric dress application uses the MIMO techniques. It consists of two multiple-input multiple-output types of antenna elements: the resonating elements are created resembling a beautiful peacock shape and the ground plane is appropriately designed. A voltage is applied to each antenna element; the ground plane contains a microstrip transmission line-fed and a rectangular upside-down L-shaped (vertically flipped) strip used for circular polarizing. The antenna covers a 3 dB axial-ratio-band-width (ARBW) value of 5.20–7.10 GHz and impedance bandwidth (S11 less than −10 dB) of 03.60–13.0 GHz. The proposed attachable wearable fabric (textile) multiple-input multiple-output wideband antenna exhibits envelope correlation coefficient (ECC) <0.02, diversity gain (DG) >9.96, channel capacity loss (CCL) <0.2 b/s/Hz, total active reflective coefficient (TARC) <−10 dB, mean effective gain (MEG) ratio within ±0.5 dB. There is dual-sense circular polarization in this antenna and high isolation between resonating elements (higher than 18). A specific absorption rate (SAR) of the proposed antenna for human tissues specimen is also discussed for different situations related to the human body. The overall size of the proposed CP textile MIMO antenna is 34 : 5 × 42 × 1 mm3. Because of its clothing layers (textile), practical performance, and miniature size, the designed MIMO antenna may be helpful for wearable on cloths on human body wireless devices and systems. The proposed antenna can be made unrecognizable because of the beautiful peacock design that can easily mix with the designs of fabric (in the fashion dress). The simulated antenna was fabricated with the help of conventional manual fabrication techniques and tested in real-time situations. The edge-to-edge distance amid the MIMO radiating antennas is 14.2 mm, and the achieved isolation is greater than 18 dB after optimization of the proposed antenna.

Single-cell transcriptomics data present challenges due to their inherent stochasticity and sparsity, complicating both cell clustering and cell type-specific network inference. To address these challenges, we introduce scMINER (single-cell Mutual Information-based Network Engineering Ranger), an integrative framework for unsupervised cell clustering, transcription factor and signaling protein network inference, and identification of hidden drivers from single-cell transcriptomic data. scMINER demonstrates superior accuracy in cell clustering, outperforming five state-of-the-art algorithms and excelling in distinguishing closely related cell populations. For network inference, scMINER outperforms three established methods, as validated by ATAC-seq and CROP-seq. In particular, it surpasses SCENIC in revealing key transcription factor drivers involved in T cell exhaustion and Treg tissue specification. Moreover, scMINER enables the inference of signaling protein networks and drivers with high accuracy, which presents an advantage in multimodal single cell data analysis. In addition, we establish scMINER Portal, an interactive visualization tool to facilitate exploration of scMINER results. Single-cell data analysis is challenging due to inherent noise and sparsity. Here, authors introduce scMINER, a mutual information-based integrative tool to enhance clustering and reveal regulatory networks and hidden biological drivers by transforming scRNA-seq expression into activity profiles.

Exposure to toxic particles from smoke generated either from bush fire, stable burning, or direct smoking is very harmful to our health. The tiny particles easily penetrate deep into the lungs after exposure and damage the airways. Tobacco smoking causes the direct emission of 2.6 million tons of CO2 and 5.2 million tons of methane annually into the atmosphere. Nevertheless, it is one of the significant contributors to various respiratory diseases leading to lung cancer. These particles’ deposition in the human airway is computed in the present article for refining our understanding of the adverse health effects due to smoke particle inhalation, especially cigarette smoke. Until recently, little work has been reported to account for the transient flow pattern of cigarette smoking. Consideration of transient flow may change the deposition pattern of the particle. A high-resolution CT scan image of the respiratory tract model consisting of the oral cavity, throat, trachea, and first to sixth generations of the lungs helps predict cigarette smoke particle (CSP) deposition. With the same scan, a realistic geometric model of the human airways of an adult subject is used to simulate the transport of air and particle. The CSP deposition is determined at different locations from the oral cavity to the sixth generation of the bronchi. In addition, an unsteady breathing curve indicative of realistic smoking behavior is utilized to represent the breathing conditions accurately. The discrete phase model (DPM) technique is used to determine smoke particle deposition in the human airways. It is found that the deposition increases with the size of the smoke particle. Particles tend to deposit in the oral cavity around the bifurcation junction of the airways. The deposition fraction of CSP with the realistic waveform of smoking is found to be smaller compared to that during the stable flow condition. It is also observed that the fine particles (0.1–1.0 micron) escape to lower generations, leading to higher deposition of fine particles in the deeper airways. The outcome of the study is helpful for understanding smoke-related pulmonary complications.

The adenomatoid odontogenic tumor (AOT) is a rare benign neoplasm that is typically treated through surgical enucleation and curettage. Any impacted tooth associated with the tumor are also extracted during the procedure. We present a case of AOT encompassing an impacted left maxillary central incisor in a 13-year-old male. The patient underwent routine treatment, but the tooth was orthodontically extruded using traction. Within two years, the incisor successfully attained its functional position in the arch, without any signs of recurrence. Preserving an impacted tooth associated with AOT is not common, and only a few cases have been reported in the literature. It is important to manage AOT conservatively and to save the associated tooth for both functional and aesthetic purposes. Long-term follow-up is crucial to monitor any potential recurrence. In summary, our case highlights the successful orthodontic management and preservation of an impacted tooth in a teenager with AOT.