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Raster logs are scanned representations of the analog data recorded in subsurface drilling. Geologists rely on these images to interpret well-log curves and deduce the physical properties of geological formations. Scanned images contain various artifacts, including hand-written texts, brightness variability, scan defects, etc. The manual effort involved in reading the data is substantial. To mitigate this, unsupervised computer vision techniques are employed to extract and interpret the curves digitally. Existing algorithms predominantly require manual intervention, resulting in slow processing times, and are erroneous. This research aims to address these challenges by proposing VeerNet, a deep neural network architecture designed to semantically segment the raster images from the background grid to classify and digitize (i.e., extracting the analytic formulation of the written curve) the well-log data. The proposed approach is based on a modified UNet-inspired architecture leveraging an attention-augmented read–process–write strategy to balance retaining key signals while dealing with the different input–output sizes. The reported results show that the proposed architecture efficiently classifies and digitizes the curves with an overall F1 score of 35% and Intersection over Union of 30%, achieving 97% recall and 0.11 Mean Absolute Error when compared with real data on binary segmentation of multiple curves. Finally, we analyzed VeerNet’s ability in predicting Gamma-ray values, achieving a Pearson coefficient score of 0.62 when compared to measured data.

Follicular unit extraction [FUE] is a minimally invasive hair restoration surgery popularly known for its utility in androgenetic alopecia (AGA). In FUE, individual follicular grafts are extracted from donor area and implanted in the recipient area. Advantages of FUE are that it is comparatively 'scarless', has faster healing time, has less downtime and requires less technical staff. This article is aimed to highlight upon the multi-faceted utility of FUE technique in various dermatological indications like androgenetic alopecia, alopecia areata, facial hair restoration, tractional alopecia, scarring alopecia, body hair transplant, vitiligo as well as hirsutism.

This paper discusses the development and implementation of a motion planning algorithm for a Nonholonomic Car type Wheeled Mobile Robot (WMR) through a specified via point. This technique will prove useful specifically; when the WMR needs to avoid obstacles which are present on the predefined trajectory and when the WMR is to be parked autonomously in the parking slot. A via-point is specified by the user and a trajectory passing through this via-point is generated using the property of differential flatness. Local optimization is employed for optimal trajectory planning. This generated trajectory is always smooth with feasible minimum length satisfying the nonholonomic and holonomic constraints on the car type vehicle and passes through the via-point specified by the user. The contribution of the work lies in reducing complexity of the problem by introducing via-point and local optimization, which results into easy implementation of the algorithm on the hardware setup. The experimentation is carried out on 1:10 reduced model of Cheverolet Camero model to show efficacy of the proposed scheme.

Despite the numerous advances, reinforcement learning remains away from widespread acceptance for autonomous controller design as compared to classical methods due to lack of ability to effectively tackle the reality gap. The reliance on absolute or deterministic reward as a metric for optimization process renders reinforcement learning highly susceptible to changes in problem dynamics. We introduce a novel framework that effectively quantizes the uncertainty of the design space and induces robustness in controllers by switching to a reliability-based optimization routine. The data efficiency of the method is maintained to match reward based optimization methods by employing a model-based approach. We prove the stability of learned neuro-controllers in both static and dynamic environments on classical reinforcement learning tasks such as Cart Pole balancing and Inverted Pendulum.

Despite the numerous advances, reinforcement learning remains away from widespread acceptance for autonomous controller design as compared to classical methods due to lack of ability to effectively tackle uncertainty. The reliance on absolute or deterministic reward as a metric for optimization process renders reinforcement learning highly susceptible to changes in problem dynamics. We introduce a novel framework that effectively quantify the uncertainty in the design space and induces robustness in controllers by switching to a reliability-based optimization routine. A model-based approach is used to improve the data efficiency of the method while predicting the system dynamics. We prove the stability of learned neuro-controllers in both static and dynamic environments on classical reinforcement learning tasks such as Cart Pole balancing and Inverted Pendulum.

Model-free reinforcement learning based methods such as Proximal Policy Optimization, or Q-learning typically require thousands of interactions with the environment to approximate the optimal controller which may not always be feasible in robotics due to safety and time consumption. Model-based methods such as PILCO or BlackDrops, while data-efficient, provide solutions with limited robustness and complexity. To address this tradeoff, we introduce active uncertainty reduction-based virtual environments, which are formed through limited trials conducted in the original environment. We provide an efficient method for uncertainty management, which is used as a metric for self-improvement by identification of the points with maximum expected improvement through adaptive sampling. Capturing the uncertainty also allows for better mimicking of the reward responses of the original system. Our approach enables the use of complex policy structures and reward functions through a unique combination of model-based and model-free methods, while still retaining the data efficiency. We demonstrate the validity of our method on several classic reinforcement learning problems in OpenAI gym. We prove that our approach offers a better modeling capacity for complex system dynamics as compared to established methods.

Background: Follicular unit extraction (FUE) has caused evolution in the management of pattern baldness. FUE methods is less traumatic with faster healing and minimal scarring. FUE is still evolving and many innovations are possible in the future. Aims and Objectives: To address the usefulness of Kerure clamp, an innovative tool for FUE method of hair transplantation. Materials and Methods: The clamp was used on 20 consecutive patients undergoing FUE for male-pattern baldness. Part of the donor area was divided into test side where extraction was done with clamp in situ and the control where clamp was not used. Follicular transection rate (FTR) was calculated as a percentage for each side and subjected to statistical analysis. Result: We found an improvement in the FTR scores on the test side, and this difference was statistically significant (t = 9.63, P < 0.0001). Conclusion: A low-cost instrument has been devised by the authors which was shown to improve the the scoring of grafts and also aid in surgeon’s efficacy and efficiency.

Hair transplantation is a relatively safe surgery and is associated with very few complications. It is a cosmetic surgery so the complications may impact social and psychological aspect of the patient. Every hair transplantation surgeon should be aware of possible complications and techniques for the prevention and techniques of their management. Most of the complications are avoidable and can be minimized by proper surgical technique and wound care. Counseling and discussion with the patient before surgery help in proper planning and avoid patient dissatisfaction. Every patient should be individualized, planned, and operated with an aim to zero-down the complications and complaints.

Sir, Keloids are a common dermatological problem faced by Dermatologists and existing modalities of treatment are unsatisfactory. Though, different modalities have been tried, recurrences are common. We hereby report the efficacy of 5 fluorouracil (FFU) in treating keloids. Thirty cases were selected for the study. Local anaesthesia was administered only for keloids >3 cm length. Field block anaesthesia was achieved by infiltrating injection bupivacaine 0.25% around the lesion. Injection FFU 50 mg/ml was infiltrated along the length of the keloid with a leur-lock syringe and 25G × 1½ needle. In cases of inflammed or hard keloids, injection triamcinolone 40 mg/ml was added to injection FFU in the ratio 1:1. Maximum dose of FFU used at a time was 1.5 ml. Patient was sent home after the injection, with instruction to apply sodium fusidate or mupirocin ointment for 5 days. Non-steroidal anti-inflammatory drugs (NSAIDs) were advised for pain. Patients were assessed after 4 weeks and, the treatment was repeated if necessary. Out of the 30 patients, six were lost to follow up. Sixteen patients showed complete flattening of keloids after an average of four injections. All these patients were advised to use Silicon-gel dressing for 3 months. Out of these 16 patients, 12 patients showed no recurrence at the end of 1 year. Commonest side effect observed with FFU injection was temporary blackish discoloration in four cases. Infection was seen in one patient who received concomitant triamcenolone. Post-steroid hypopigmentation was seen in two cases. Keloid is an overgrowth of dense fibrous tissue that extends beyond the borders of the original wound, whereas hypertrophic scar remains confined to the borders of the original wound. Abnormal wound healing results in disruption of the cascade of events in collagen synthesis, which leads to formation of hypertrophic scar or keloid.[1] Exact aetiology of keloid is still not fully understood, but genetic predisposition is seen in cases of keloid, along with predisposition for anatomical sites like chest, shoulders, jaw line, upper back, gluteal region, ears, etc. Increased skin stretching at the above-mentioned anatomical sites and wound infection may be the factors contributing to the formation of keloids. Usually treatment for keloids is sought for pain, pruritus, discomfort, cosmetic disfigurement and restriction of movements. Available treatment modalities include: intralesional steroid injections, cryotherapy with or without steroid injections, intralesional cryotherapy, excision followed by silicon-gel dressings, pulsed dye laser (585 nm), superficial X-ray therapy, etc. All these therapies have shown temporary regression in the size of keloids, but recurrence is known with all the modalities.[2] Five fluorouracil has been shown to inhibit fibroblasts in tissue culture and is believed to reduce post-op scarring. Corticosteroids suppress inflammation and cause vasoconstriction. Their antimitotic effect results in inhibition of fibroblasts and keratinocytes. In our study, complete flattening of keloid was seen in 75% cases with no recurrence. These preliminary results are encouraging, but further studies are needed to confirm these early findings.

Modern day dermatologists conduct different esthetic and surgical procedures, with risk of infective complications. Hence, infection control practices need to be established in dermatological practice to minimize the risk of exogenous infections. These practices include hand washing, cleaning, sterilization, disinfection, operation theater sterilization and specifications. Proper hand washing after examination of each patient and prior to any surgery with a formulation containing alcohol alone or as a combination with other agents reduces the chances of transferring infections to and from patients. Sterilization and disinfection constitute the most important aspect of infection control. Disinfectants and disinfecting procedures vary according to the environment and equipment. Proper knowledge of different processes/agents for sterilization and disinfection is essential. Disinfectants for use in hospitals should always be freshly prepared and should be of adequate strength. Sterilization is carried out most commonly using steam sterilizers or ethylene dioxide sterilizers. The waste generated during practice is a potential source of nosocomial infections and should be treated as per the proper protocol and guidelines. Trained staff to carry out these practices is essential.