Issues And Challenges Of Hand Washing With Particular Reference To Prevention Of Covid -19

Hand washing also known as hand hygiene, is the act of cleaning one’s hands with soap and water to remove viruses/bacteria/germs/ microorganisms. If water and soap are not available, hands can be cleaned with ash although the benefits and harms are uncertain for reducing the spread of viral or bacterial infections. This is especially important for people who handle food or work in the medical field, but also an important practice for the general public. Hand hygiene is one of the most important element of infection control activities

Non-descent vaginal hysterectomy in previous cesarean section: a retrospective study

Background: Non-descent vaginal hysterectomy (NDVH) is removal of uterus through vagina in non-prolapsed uterus. As there is an increase in caesarean section, hysterectomy in women with previous caesarean section is also increasing. The objective of this study is to assess the feasibility and safety of non-descent vaginal hysterectomy in patients with previous caesarean section.Methods: This is a retrospective study conducted in the department of obstetrics and gynecology, Government Medical College, Thrissur from January 2017 to December 2018. Non-descent vaginal hysterectomy in 24women with previous caesarean section was studied. Details regarding age, parity, number of caesarean sections, indication of surgery, intraoperative and postoperative complications were evaluated.Results: All 24 women underwent non-descent vaginal hysterectomy successfully. 10 women (41.7%) were between 46-49 years. Commonest indication of hysterectomy was fibroid uterus (41.7%) and most common complaint was heavy menstrual bleeding (79%). There was bladder injury in one woman with history of previous 2 caesarean section. 3 women developed UTI in postoperative period.Conclusions: Vaginal hysterectomy is associated with lower complications and more rapid recovery. A successful NDVH in previous caesarean section depends on the expertise and experience of the surgeon. NDVH in previous caesarean is safe in expert hands

A META CLUSTERING APPROACH FOR ENSEMBLE PROBLEM

A critical problem in cluster ensemble research is how to combine multiple clustering to yield a superior clustering result. Leveraging advanced graph partitioning techniques, we solve this problem by reducing it to a graph partitioning problem. We introduce a new reduction method that constructs a bipartite graph from a given cluster ensemble. The resulting graph models both instances and clusters of the ensemble simultaneously as vertices in the graph. Our approach retains all of the information provided by a given ensemble, allowing the similarity among instances and the similarity among clusters to be considered collectively in forming the clustering. Further, the resulting graph partitioning problem can be solved efficiently. We empirically evaluate the proposed approach against two commonly used graph formulations and show that it is more robust and achieves comparable or better performance in comparison to its competitors

Modelling microgels with controlled structure across the volume phase transition

Thermoresponsive microgels are soft colloids that find widespread use as model systems for soft matter physics. Their complex internal architecture, made of a disordered and heterogeneous polymer network, has been so far a major challenge for computer simulations. In this work we put forward a coarse-grained model of microgels whose structural properties are in quantitative agreement with results obtained with small-angle X-ray scattering experiments across a wide range of temperatures, encompassing the volume phase transition. These results bridge the gap between experiments and simulations of individual microgel particles, paving the way to theoretically address open questions about their bulk properties with unprecedented nano and microscale resolution

Non-isolated high gain DC-DC converter by quadratic boost converter and voltage multiplier cell

AbstractA novel non-isolated DC-DC converter is proposed by combining quadratic boost converter with voltage multiplier cell. The proposed converter has low semiconductor device voltage stress and switch utilization factor is high. The superiority of the converter is voltage stress of the semiconductor devices depends on voltage multiplier (VM) cell. By increasing the VM cell the stresses across the devices reduce drastically. The proposed converter has same number of components compared to certain voltage lift converters taken for comparison. A detailed comparative study is made on the proposed converter with few voltage lift converters in the literature, conventional boost with VM cell and quadratic boost converter. A 40W prototype is constructed with 12V input voltage and 96V output voltage to verify the performance and validate the theoretical analysis of the proposed converter

FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout

Federated Learning (FL) allows machine learning models to train locally on individual mobile devices, synchronizing model updates via a shared server. This approach safeguards user privacy; however, it also generates a heterogeneous training environment due to the varying performance capabilities across devices. As a result, straggler devices with lower performance often dictate the overall training time in FL. In this work, we aim to alleviate this performance bottleneck due to stragglers by dynamically balancing the training load across the system. We introduce Invariant Dropout, a method that extracts a sub-model based on the weight update threshold, thereby minimizing potential impacts on accuracy. Building on this dropout technique, we develop an adaptive training framework, Federated Learning using Invariant Dropout (FLuID). FLuID offers a lightweight sub-model extraction to regulate computational intensity, thereby reducing the load on straggler devices without affecting model quality. Our method leverages neuron updates from non-straggler devices to construct a tailored sub-model for each straggler based on client performance profiling. Furthermore, FLuID can dynamically adapt to changes in stragglers as runtime conditions shift. We evaluate FLuID using five real-world mobile clients. The evaluations show that Invariant Dropout maintains baseline model efficiency while alleviating the performance bottleneck of stragglers through a dynamic, runtime approach