Tirunelveli Medical College

Purpose: COVID-19-associated rhino-orbital-cerebral mucormycosis (ROCM) has reached epidemic proportion during India's second wave of COVID-19 pandemic, with several risk factors being implicated in its pathogenesis. This study aimed to determine the patient demographics, risk factors including comorbidities, and medications used to treat COVID-19, presenting symptoms and signs, and the outcome of management. Methods: This was a retrospective, observational study of patients with COVID-19-associated ROCM managed or co-managed by ophthalmologists in India from January 1, 2020 to May 26, 2021. Results: Of the 2826 patients, the states of Gujarat (22%) and Maharashtra (21%) reported the highest number of ROCM. The mean age of patients was 51.9 years with a male preponderance (71%). While 57% of the patients needed oxygen support for COVID-19 infection, 87% of the patients were treated with corticosteroids, (21% for > 10 days). Diabetes mellitus (DM) was present in 78% of all patients. Most of the cases showed onset of symptoms of ROCM between day 10 and day 15 from the diagnosis of COVID-19, 56% developed within 14 days after COVID-19 diagnosis, while 44% had delayed onset beyond 14 days. Orbit was involved in 72% of patients, with stage 3c forming the bulk (27%). Overall treatment included intravenous amphotericin B in 73%, functional endoscopic sinus surgery (FESS)/paranasal sinus (PNS) debridement in 56%, orbital exenteration in 15%, and both FESS/PNS debridement and orbital exenteration in 17%. Intraorbital injection of amphotericin B was administered in 22%. At final follow-up, mortality was 14%. Disease stage >3b had poorer prognosis. Paranasal sinus debridement and orbital exenteration reduced the mortality rate from 52% to 39% in patients with stage 4 disease with intracranial extension (p < 0.05). Conclusion: : Corticosteroids and DM are the most important predisposing factors in the development of COVID-19-associated ROCM. COVID-19 patients must be followed up beyond recovery. Awareness of red flag symptoms and signs, high index of clinical suspicion, prompt diagnosis, and early initiation of treatment with amphotericin B, aggressive surgical debridement of the PNS, and orbital exenteration, where indicated, are essential for successful outcome.

Biohydrogen production from industrial wastewater has been a focus of interest in recent years. The in depth knowledge in lab scale parameters and emerging strategies are needed to be investigated in order to implement the biohydrogen production process at large scale. The operating parameters have great influence on biohydrogen productivity. With the aim to gain major insight into biohydrogen production process, this review summarizes recent updates on dark fermentation, inoculum pretreatment methods, operating parameters (hydraulic retention time, organic loading rate, pH, temperature, volatile fatty acids, bioreactor configuration, nutrient availability, partial pressure etc.). The challenges and limitations associated with the biohydrogen production are lack of biohydrogen producers, biomass washout and accumulation of metabolites are discussed in detail. The advancement strategies to overcome these limitations are also briefly discussed.

Predicting the performance of a student is a great concern to the higher education managements. The scope of this paper is to identify the factors influencing the performance of students in final examinations and find out a suitable data mining algorithm to predict the grade of students so as to a give timely and an appropriate warning to students those who are at risk. In the present investigation, a survey cum experimental methodology was adopted to generate a database and it was constructed from a primary and a secondary source. The obtained results from hypothesis testing reveals that type of school is not influence student performance and parents' occupation plays a major role in predicting grades. This work will help the educational institutions to identify the students who are at risk and to and provide better additional training for the weak students.

AIMS: To determine the sensitivity, specificity and predictive values of potassium hydroxide (KOH) wet mount, Gram stain, Giemsa stain and Kinyoun's acid-fast stain in the diagnosis of infective keratitis. METHODS: A retrospective analysis of all patients with clinically diagnosed infective keratitis presenting between September 1999 and September 2002 was carried out. Corneal scrapes were taken and subjected to direct microscopy and culture. RESULTS: 3298 eyes of 3295 consecutive patients with infective keratitis were evaluated, of which 1138 (34.51%) eyes had fungal growth alone, 1069 (32.41%) had bacterial growth alone, 33 (1%) had Acanthamoeba growth alone, 83 (2.5%) had mixed microbial growth and the remaining 975 (29.56%) had no growth. The sensitivity of KOH wet mount was higher (99.3%; 95% confidence interval (CI) 98.6 to 99.6) in the detection of fungi, 100% (95% CI 90.4 to 100) in the detection of Nocardia and 91.4% (95% CI 75.8 to 97) in the detection of Acanthamoeba) than that of Gram-stained smears (89.2% (95% CI 87.3 to 90.8) in fungi, 87% (95% CI 73.0 to 94.6) in Nocardia and 60% (95% CI 42.2 to 75.6) in the detection of Acanthamoeba) in the detection of fungi, Nocardia and Acanthamoeba. 1764 of 3295 (53.54%) patients presented more than 7 days after onset of illness and 84.69% of the eyes had corneal ulcers with size >2 mm in diameter. Positivities of KOH (44.46%; p<0.001) and Gram-stained smears (77.37%; p<0.001) were found to be higher among eyes with larger ulcers (>2 mm) than among eyes with smaller ulcers (<2 mm). CONCLUSION: KOH smear is of greater diagnostic value in the management of infective keratitis, and it is recommended in all clinics without exception for establishing timely treatment.

/rGO/Nf immobilized GCE electrochemical sensor exhibits an ample range of sensing from 0.1 µM to 0.9 µM and the low detection limit is found to be 0.035 µM (S/N = 3). Comparable results are achieved for the determination of nicotine in various real samples such as cigarettes (Gold flake and Wills) and urine samples with improved recoveries.

A review of Al based metal matrix composite reinforced with TiB2 particles developed after 1997 is presented in this paper. This paper presents an overview of Al-TiB2 MMC on aspects relating to the formation of TiB2, development of Al-TiB2, mechanical characteristics, thermodynamic calculation, wear behavior of Al-TiB2, cycle fatigue response of insitu Al based composite, processing, microstructure, properties and application are discussed. Aluminium alloys are widely used for commercial applications in the transportation, construction and similar engineering industries. Nowadays, main focus is given to Aluminium as matrix material because of its unique combination of good corrosion resistance, low electrical resistance and it possesses excellent mechanical properties in addition to good corrosion resistance due to which the alloy finds extensive application in naval vessels manufacturing. Al-TiB2 composite is a metal matrix composite (MMC) that can be manufactured using the in-situ salt-metal reaction. With TiB2 as the particulate addition the properties of Al 6061 alloy can be greatly improved. The addition of TiB2 to aluminium increases the strength of the aluminium. The wear resistance of this material is due to the lack of pull out of this material from the aluminium matix. Also, such material when machine with non–conventional methods like EDM will show better machinability due to its high conductivity. In the last few years, Al-TiB2 have been utilized in high-tech structural and functional applications including aerospace, defense, automotive, and thermal management areas, as well as in sports and recreation. The future research is summarized finally.

With the advent of next-generation sequencing, large-scale initiatives for mining whole genomes and exomes have been employed to better understand global or population-level genetic architecture. India encompasses more than 17% of the world population with extensive genetic diversity, but is under-represented in the global sequencing datasets. This gave us the impetus to perform and analyze the whole genome sequencing of 1029 healthy Indian individuals under the pilot phase of the 'IndiGen' program. We generated a compendium of 55,898,122 single allelic genetic variants from geographically distinct Indian genomes and calculated the allele frequency, allele count, allele number, along with the number of heterozygous or homozygous individuals. In the present study, these variants were systematically annotated using publicly available population databases and can be accessed through a browsable online database named as 'IndiGenomes' http://clingen.igib.res.in/indigen/. The IndiGenomes database will help clinicians and researchers in exploring the genetic component underlying medical conditions. Till date, this is the most comprehensive genetic variant resource for the Indian population and is made freely available for academic utility. The resource has also been accessed extensively by the worldwide community since it's launch.

In the practice of medicine, antibiotics are extremely important and are employed in the treatment of infections. A lot of antibiotics are consumed by humans and excreted via urine and feces into sewage systems and treatment plants. These are considered to be non-biodegradable, and over the years they accumulate in the aquatic environment. The presence of antibiotics in water resources causes the emergence of antibiotic-resistant bacteria, posing a serious threat to the health of human beings. Water bodies must be adequately treated before being discharged to prevent the spread of antibiotic resistance. In the present article, the sources of antibiotics and strategies used for their effective removal, such as ultrafiltration, microfiltration, nanofiltration, membranous biological reactor treatment, Advanced Oxidation Process (AOP), Reverse Osmosis (RO) and Nano sorbents, are discussed. Conventional wastewater treatment plants are not able to eliminate antibiotics deposition/resistance genes effectively and efficiently. In this regard, the adsorption method is the most effective way of removing antibiotics from wastewater from various sources.

The development of the modern power system has led to an increasing complexity in the study of power systems, and also presents new challenges to power system stability, and in particular, to the aspects of transient stability and small-signal stability. Transient stability control plays a significant role in ensuring the stable operation of power systems in the event of large disturbances and faults, and is thus a significant area of research. This paper investigates the improvement of transient stability of a two-area power system, using UPFC (Unified Power Flow Controller) which is an effective FACTS (Flexible AC Transmission System) device capable of controlling the active and reactive power flows in a transmission line by controlling appropriately its series and shunt parameters. Simulations are carried out in Matlab/Simulink environment for the two-area power system model with UPFC to analyze the effects of UPFC on transient stability performance of the system. The performance of UPFC is compared with other FACTS devices such as Static Synchronous Series Compensator (SSSC), Thyristor Controlled Series Capacitor (TCSC), and Static Var Compensator (SVC) respectively. The simulation results demonstrate the effectiveness and robustness of the proposed UPFC on transient stability improvement of the system.

In this work a stepped solar still is used to enhance the productivity of the solar still. The concept of integrating the stepped solar still along with inclined flat plate collector is introduced in this research work. In this stepped type solar still, a conventional basin of area 1 m2, was placed at the bottom. Another absorber plate, stepped type was fixed on the top of the conventional basin. It consists of subsequent trays and inclined flat plate collectors. This ensures an additional exposure area which augments the evaporation rate. Experiments were conducted with various depths in the conventional basin. A conventional still was fabricated and run parallel with the experimental set up for comparison. Sensible heat storage mediums such as rocks, pebbles were added to the top basin of stepped trays and bottom conventional basins to increase the temperature of water in the still. Wicks were placed on the inclined flat plate collector to augment the evaporation rate due to capillarity. A higher evaporation rate is obtained in the packing material with wicks and pebbles in tray combinations. Theoretical analysis was performed and it agrees with experimental values. Efficiency of the system was also compared with conventional solar still.

Abstract Health complications during the gestation period have evolved as a global issue. These complications sometimes result in the mortality of the fetus, which is more prevalent in developing and underdeveloped countries. The genesis of machine learning (ML) algorithms in the healthcare domain have brought remarkable progress in disease diagnosis, treatment, and prognosis. This research deploys various ML algorithms to predict fetal health from the cardiotocographic (CTG) data by labelling the health state into normal, needs guarantee, and pathology. This work assesses the influence of various factors measured through CTG to predict the health state of the fetus through algorithms like support vector machine, random forest (RF), multi‐layer perceptron, and K‐nearest neighbours. In addition to this, the regression analysis and correlation analysis revealed the influence of the attributes on fetal health. The results of the algorithms show that RF performs better than its peers in terms of accuracy, precision, recall, F1‐score, and support. This work can further enhance more promising results by performing suitable feature engineering in the CTG data.

Naturally woven coconut sheath, a new natural fiber available in the form of woven mat is used as fiber reinforcement in polyester matrix. The hybridization effect of montmorillonite (MMT) on naturally woven coconut sheath/polyester composite has been investigated experimentally for free vibration characteristics using impulse excitation technique (IET). In the present study, naturally woven coconut sheath/clay-reinforced hybrid polyester composites were fabricated using a compression molding machine. The effect of organoclay addition (1, 2, 3 and 5 wt%), alkali (ATC) and silane treatment (STC) of the coconut sheath surface on free vibration characteristics were analyzed. Scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X-ray diffraction (XRD) techniques were used to characterize the morphology of coconut sheath and structure of clay dispersed polyester. The dynamic mechanical analysis (DMA) and Fourier transform infrared (FTIR) analysis were carried out to analyze the effect of surface topology on chemically treated coconut sheath. The mechanism of chemical modifications on natural frequencies and the resultant improvement in the temperature dependence mechanical properties have also been reported. Enhanced vibration properties are observed up to 3 wt% of clay dispersion in hybrid composites, then it gets reduced for higher clay (&gt;3 wt%) content. Modal damping of the hybrid composite is influenced by the addition of nanoclay and the surface treatments of the coconut sheath. Experimentally obtained natural frequencies are in good agreement with analytical and numerical results.

Diabetes mellitus is a chronic metabolic disease. Oxidative stress plays a major part in the pathogenesis of diabetes. Supplementation with antioxidants and the medicinal plants which possess antioxidants activity have been reported their hypoglycemic activity. The antioxidants are used to treat and reduce the complication of diabetes mellitus. The diet supplementations of antioxidants vitamins are beneficial in the treatment of diabetes. This review article was summarizing the role of antioxidants in diabetes mellitus.

Detecting and avoiding potholes is a more challenging task in India, due to the poor quality of construction materials used in road privilege systems. Identifying and repairing potholes as soon as possible is crucial to preventing accidents. Roadside potholes can cause serious traffic safety problems and damage automobiles. In this paper a novel Pothole detection using Yolov8 (POT-YOLO) has been introduced for detecting the types of potholes such as Cracks, Oil stains, Patches, Pebbles using POT-YOLOv8. Initially, pothole videos are converted into frames of images for further processing. To reduce distortions, these frames are pre-processed with the Contrast Stretching Adaptive Gaussian Star Filter (CAGF). Finally, the pre-processed images are identifying the region of pothole using Sobal edge detector and detect the pothole using YOLOv8. The POT-YOLO approach was simulated with Python code. The simulation result demonstrate that the POT-YOLO methods performance was measured in terms of ACU, PRE, RCL, and F1S. The POT-YOLO achieves an overall ACU of 99.10%. Additionally, POT-YOLO model achieves 97.6 % precision, 93.52 % recall, and 90.2% F1-score. In the comparison, the POT-YOLOv8 network improves the better ACU range than existing networks such as Faster RCNN, SSD, and mask R CNN. The POT-YOLO approach improves the overall ACU by 12.3%, 0.97 %, and 1.4 % better than ML based DeepBus, Automatic color image analysis using DNN, and ODRNN respectively.

Technology advancements have enabled the capture of Renewable Energy Sources (RES) on a massive scale. Smart Grids (SGs) that combine conventional and RES are predicted as a sustainable method of power generation. Moreover, environmental conditions impact all RES, causing changes in the amount of electricity produced by these sources. Furthermore, availability is dependent on daily or annual cycles. Although smart meters allow real-time demand prediction, precise models that predict the electricity produced by RES are also required. Prediction Models (PMs) accurately guarantee grid stability, efficient scheduling, and energy management. For example, the SG must be smoothly transformed into the conventional energy source for that time and guarantee that the electricity generated meets the predicted demand if the model predicts a period of Renewable Energy (RE) loss. The literature also suggests scheduling methods for demand-supply matching and different learning-based PMs for sources of RE using open data sources. This paper developed a model that accurately replicates a microgrid, predicts demand and supply, seamlessly schedules power delivery to meet demand, and gives actionable insights into the SG system’s operation. Furthermore, this work develops the Demand Response Program (DRP) using improved incentive-based payment as cost suggestion packages. The test results are valued in different cases for optimizing operating costs through the multi-objective ant colony optimization algorithm (MOACO) with and without the input of the DRP.

PURPOSE: The objectives of this study were to compare the effects of caudal dexmedetomidine combined with ropivacaine to provide postoperative analgesia in children and also to establish its safety in the paediatric population. METHODS: In a randomised, prospective, parallel group, double-blinded study, 60 children were recruited and allocated into two groups: Group RD (n=30) received 0.25% ropivacaine 1 ml/kg with dexmedetomidine 2 μg/kg, making the volume to 0.5 ml and Group R (n=30) received 0.25% ropivacaine 1 ml/kg + 0.5 ml normal saline. Induction of anaesthesia was achieved with 50% N(2)O and 8% sevoflurane in oxygen in spontaneous ventilation. An appropriate-sized LMA was then inserted and a caudal block performed in all patients. Behaviour during emergence was rated with a 4-point scale, sedation with Ramsay's sedation scale, and pain assessed with face, legs, activity, cry, consolability (FLACC) pain score. RESULTS: The duration of postoperative analgesia recorded a median of 5.5 hours in Group R compared with 14.5 hours in Group RD, with a P value of <0.001. Group R patients achieved a statistically significant higher FLACC score compared with Group RD patients. The difference between the means of mean sedation score, emergence behaviour score, mean emergence time was statistically highly significant (P<0.001). The peri-operative haemodynamics were stable among both the groups. CONCLUSION: Caudal dexmedetomidine (2 μg/kg) with 0.25% ropivacaine (1 ml/kg) for paediatric lower abdominal surgeries achieved significant postoperative pain relief that resulted in a better quality of sleep and a prolonged duration of arousable sedation and produced less incidence of emergence agitation following sevoflurane anaesthesia.

Texture is the surface property that is used to identify and recognise objects. This property is widely used in many applications including texture-based face recognition systems, surveillance, identity verification and so on. The Local binary pattern (LBP) texture method is most successful for face recognition. Owing to the great success of LBP, recently many models, which are variants of LBP have been proposed for texture analysis. Some of the derivatives of LBPs are multivariate local binary pattern, centre symmetric local binary pattern, local binary pattern variance, dominant local binary pattern, advanced local binary pattern, local texture pattern (LTP) and local derivative pattern (LDP). In this scenario, it is essential to review, whether LBP or their derivatives perform better for face recognition. The real-time challenges such as illumination changes, rotations, angle variations and facial expression variations are evaluated by different LBP-based models. Experiments were conducted on the Japanese female facial expression, YALE and FRGC version2 databases. The results show that LDP and LTP perform much better than the other LBP-based models.

PURPOSE: To study the epidemiological characteristics of bacterial keratitis seen at a tertiary eye care referral centre in south India. METHODS: A retrospective review of medical records of all culture-positive bacterial keratitis which were seen over a 3 years period, from September 1999 through August 2002 was performed. After clinical evaluation corneal scrapings were collected and subjected to culture and microscopy using standard protocols in all patients. RESULTS: Out of 3183 corneal ulcers evaluated, 1043(32.77%) were found to be of bacterial aetiology. A total of 1109 bacterial pathogens were isolated from 1046 eyes with keratitis. The predominant bacterial species isolated was Streptococcus pneumoniae (37.5%). Males were 592(56.76%) and 451(43.24%) were females. There were 564(54.07%) rural residents and 479(45.93%) urban residents; this difference was statistically significant (p< 0.0001). Patients with age more than 50 years (60.2%) were affected significantly more than patients aged less than 50 years (30.8%). While 57.62% of patients were non-agricultural workers, 42.38% were farmers; this difference was statistically significant (p<0.0001). Co-existing ocular diseases predisposing to corneal ulceration were identified in 703(67.4%) patients, compared to other predisposing risk factors in 340(32.6%) patients. One hundred and seventy seven (16.97%) had corneal injury with soil and/or sand, compared to 115(11.03%) patients who had injury due to other materials and the difference was statistically significant. There was lower incidence of bacterial keratitis from June to September. CONCLUSIONS: The epidemiological characteristics of bacterial keratitis vary geographically. This study describing the features of bacterial keratitis would greatly help the practising ophthalmologist and other medical practitioners in the management of their patients.

As digital products communicate over public networks, digital image security becomes essential. Despite advancements in encryption technology, protecting data from secure images remains a complex computational issue that necessitates using an encrypted environment to protect data transmitted from devices over networks. Encryption is critical for protecting sensitive information, especially images, from unauthorized access or failure. Image encryption is distinct from text encryption. Images contain extensive data with high redundancy and a high correlation between nearby pixels, making them difficult to process with traditional technology. In recent decades, chaos maps have become popular in the crypto community. This work proposes a new encryption technology based on chaotic map substitution boxes (S-box) and cellular automata (CA) to address the frequent challenges in chaotic encryption methods. To address the inadequate randomness provided by the 1D chaotic map, this work presented a 4D memristive hyperchaos with a more excellent chaotic range, increased uncertainty, and ergodicity as an alternative to the software-based approach, which is vulnerable and offers low throughput. The suggested encryption technique was implemented on an Intel Cyclone IV EP4CE115F29C7 FPGA. It is less prone to tampering and has a better throughput. This suggested encryption scheme employs 1384 LEs. In comparison to traditional algorithms, the suggested Number of Pixels Change Rate (NPCR) (99.9706%), unified averaged changed intensity (UACI) (33.3956%), and information entropy (IE) (7.984) accomplish more effective. Statistical studies such as IE, histogram, correlation, peak signal-to-noise ratio (PSNR), differential analysis such as NPCR and UACI, and noise attack analyses are employed to validate the suggested encryption design. Regarding security, the suggested hybrid method outperforms conventional algorithms while protecting image quality.

Abstract Urbanization increases electricity demand due to population growth and economic activity. To meet consumer’s demands at all times, it is necessary to predict the future building energy consumption. Power Engineers could exploit the enormous amount of energy-related data from smart meters to plan power sector expansion. Researchers have made many experiments to address the supply and demand imbalance by accurately predicting the energy consumption. This paper presents a comprehensive literature review of forecasting methodologies used by researchers for energy consumption in smart buildings to meet future energy requirements. Different forecasting methods are being explored in both residential and non-residential buildings. The literature is further analyzed based on the dataset, types of load, prediction accuracy, and the evaluation metrics used. This work also focuses on the main challenges in energy forecasting due to load fluctuation, variability in weather, occupant behavior, and grid planning. The identified research gaps and the suitable methodology for prediction addressing the current issues are presented with reference to the available literature. The multivariate analysis in the suggested hybrid model ensures the learning of repeating patterns and features in the data to enhance the prediction accuracy.