Reliability improvement and loss reduction in radial distribution system with network reconfiguration algorithms using loss sensitivity factor

<p>Studies on load flow in electrical distribution system have always been an area of interest for research from the previous few years. Various approaches and techniques are brought into light for load flow studies within the system and simulation tools are being used to work out on varied characteristics of system. This study concentrates on these approaches and the improvements made to the already existing techniques considering time and the algorithms complexity. Also, the paper explains the network reconfiguration (NR) techniques considered in reconfiguring radial distribution network (RDN) to reduce power losses in distribution system and delivers an approach to how various network reconfiguration techniques support loss reduction and improvement of reliability in the electrical distribution network.</p>

Can the Post-COVID FDI Boost the Indian Economy?

Ever since the liberation of trade policies in India, Foreign Direct Investments (FDI) has been crucial in the growth of the economy, both at the macro as well as sector level. The association between FDI and economic growth is an area of interest globally. The investment decisions are affected by several national and international events that add to the volatility of the number of inflows. COVID-19 pandemic severely impacted the intensity of FDI inflows. But the strong resilience by our government manifested in crucial policy reforms and proactive decision-making minimized the impact. This article examines the potential impact of FDI on crucial macroeconomic variables using the Computable General Equilibrium (CGE) Model. Introducing the policy shock of $90 billion into the model, an increase of 5.68% per annum in GDP is estimated. Findings indicate that the impact of FDI shall be favourable to a large number of sectors mainly metals, construction, motor vehicle, computers, and electronics in terms of increased output, exports, and employment opportunities. The study offers logical implications for the policymakers to continue strengthening their moves to attract FDI.

COVID-19 in Single Kidney Patient

The COVID-19 pandemic has affected all people in the world, especially those at risk of kidney disorders. Early kidney damage in patients born with unilateral renal agenesis (URA) or solitary kidney can happen. These patients are at risk of chronic kidney disease (CKD), high blood pressure, and developing proteinuria. Unilateral renal agenesis is a cause of CKD. Therefore, it is very interesting that observe a unilateral renal Patient that Suffers from COVID-19. Hence, the management of these patients with COVID‐19 is an area of interest, and a unique approach is warranted. A 43-year-old male patient with unilateral renal presented to our hospital for corona disease. The case was discussed between the nephrologists, Infectious disease specialists, and nursing head nurses for a care plan daily. The patient had unilateral renal disease, and COVID-19 could have a detrimental effect on the renal, but renal tests were normal, and the patient recovered without acute renal complications. The treatment of such patients is the need for teamwork contain nephrologists, critical care nurses, and specialists in infectious and tropical diseases. This was a new experience in Iran.

MntP and YiiP Contribute to Manganese Efflux in Salmonella enterica Serovar Typhimurium under Conditions of Manganese Overload and Nitrosative Stress

Transition metal cations are required for the function of many proteins but can mediate toxicity when present in excess. Identifying transporters that facilitate metal ion export, the conditions under which they are expressed, and the role they play in bacterial physiology is an evolving area of interest for environmental and pathogenic organisms.

Full 4D Change Analysis of Topographic Point Cloud Time Series using Kalman Filtering

4D topographic point cloud data contain information on surface change processes and their spatial and temporal characteristics, such as the duration, location, and extent of mass movements, e.g., rockfalls or debris flows. To automatically extract and analyse change and activity patterns from this data, methods considering the spatial and temporal properties are required. The commonly used M3C2 point cloud distance reduces uncertainty through spatial averaging for bitemporal analysis. To extend this concept into the full 4D domain, we use a Kalman filter for point cloud change analysis. The filter incorporates M3C2 distances together with uncertainties obtained through error propagation as Bayesian priors in a dynamic model. The Kalman filter yields a smoothed estimate of the change time series for each spatial location, again associated with an uncertainty. Through the temporal smoothing, the Kalman filter uncertainty is, in general, lower than the individual bitemporal uncertainties, which therefore allows detection of more change as significant. In our example time series of bi-hourly terrestrial laser scanning point clouds of around 6 days (71 epochs) showcasing a rockfall-affected high-mountain slope in Tyrol, Austria, we are able to almost double the number of points where change is deemed significant (from 14.9 % to 28.6 % of the area of interest). Since the Kalman filter allows interpolation and, under certain constraints, also extrapolation of the time series, the estimated change values can be temporally resampled. This can be critical for subsequent analyses that are unable to deal with missing data, as may be caused by, e.g., foggy or rainy weather conditions. We demonstrate two different clustering approaches, transforming the 4D data into 2D map visualisations that can be easily interpreted by analysts. By comparison to two state-of-the-art 4D point cloud change methods, we highlight the main advantage of our method to be the extraction of a smoothed best estimate time series for change at each location. A main disadvantage of not being able to detect spatially overlapping change objects in a single pass remains. In conclusion, the consideration of combined temporal and spatial data enables a notable reduction in the associated uncertainty of the quantified change value for each point in space and time, in turn allowing the extraction of more information from the 4D point cloud dataset.

Targeting KRAS in Non-Small Cell Lung Cancer

Kirsten Rat Sarcoma viral oncogene homolog (KRAS) is the most frequently altered oncogene in Non-Small Cell Lung Cancer (NSCLC). KRAS mutant tumors constitute a heterogeneous group of diseases, different from other oncogene-derived tumors in terms of biology and response to treatment, which hinders the development of effective drugs against KRAS. Therefore, for decades, despite enormous efforts invested in the development of drugs aimed at inhibiting KRAS or its signaling pathways, KRAS was considered to be undruggable. Recently, the discovery of a new pocket under the effector binding switch II region of KRAS G12C has allowed the development of direct KRAS inhibitors such as sotorasib, the first FDA-approved drug targeting KRAS G12C, or adagrasib, initiating a new exciting era. However, treatment with targeted KRAS G12C inhibitors also leads to resistance, and understanding the possible mechanisms of resistance and which drugs could be useful to overcome it is key. Among others, KRAS G12C (ON) tricomplex inhibitors and different combination therapy strategies are being analyzed in clinical trials. Another area of interest is the potential role of co-mutations in treatment selection, particularly immunotherapy. The best first-line strategy remains to be determined and, due to the heterogeneity of KRAS, is likely to be based on combination therapies.

Mosaicking Weather Radar Retrievals from an Operational Heterogeneous Network at C and X Band for Precipitation Monitoring in Italian Central Apennines

Meteorological radar networks are suited to remotely provide atmospheric precipitation retrieval over a wide geographic area for severe weather monitoring and near-real-time nowcasting. However, blockage due to buildings, hills, and mountains can hamper the potential of an operational weather radar system. The Abruzzo region in central Italy’s Apennines, whose hydro-geological risks are further enhanced by its complex orography, is monitored by a heterogeneous system of three microwave radars at the C and X bands with different features. This work shows a systematic intercomparison of operational radar mosaicking methods, based on bi-dimensional rainfall products and dealing with both C and X bands as well as single- and dual-polarization systems. The considered mosaicking methods can take into account spatial radar-gauge adjustment as well as different spatial combination approaches. A data set of 16 precipitation events during the years 2018–2020 in the central Apennines is collected (with a total number of 32,750 samples) to show the potentials and limitations of the considered operational mosaicking approaches, using a geospatially-interpolated dense network of regional rain gauges as a benchmark. Results show that the radar-network pattern mosaicking, based on the anisotropic radar-gauge adjustment and spatial averaging of composite data, is better than the conventional maximum-value merging approach. The overall analysis confirms that heterogeneous weather radar mosaicking can overcome the issues of single-frequency fixed radars in mountainous areas, guaranteeing a better spatial coverage and a more uniform rainfall estimation accuracy over the area of interest.

Emerging Novel Therapeutic Approaches for Treatment of Advanced Cutaneous Melanoma

The prognosis of patients with advanced cutaneous melanoma has radically changed in the past decade. Nevertheless, primary or acquired resistance to systemic treatment occurs in many cases, highlighting the need for novel treatment strategies. This review has the purpose of summarizing the current area of interest for the treatment of metastatic or unresectable advanced cutaneous melanoma, including data from recently completed or ongoing clinical trials. The main fields of investigation include the identification of new immune checkpoint inhibitors (anti-LAG3, GITR agonist and anti-TIGIT), adoptive cell therapy, vaccines, engineered TCR therapy, IL-2 agonists, novel targets for targeted therapy (new MEK or RAF inhibitors, HDAC, IDO, ERK, Axl, ATR and PARP inhibitors), or combination strategies (antiangiogenetic agents plus immune checkpoint inhibitors, intra-tumoral immunotherapy in combination with systemic therapy). In many cases, only preliminary efficacy data from early phase trials are available, which require confirmation in larger patient cohorts. A more in-depth knowledge of the biological effects of the molecules and identifying predictive biomarkers remain crucial for selecting patient populations most likely to benefit from novel emerging treatment strategies.

Target Positioning and Grasping of NAO Robot Based on Monocular Stereo Vision

With the continuous advancement of science and technology and the rapid development of robotics, it has become an inevitable trend for domestic robots to enter thousands of households. In order to solve the inconvenience problem of the elderly and people with special needs, because the elderly and other people in need may need the help of domestic robots due to inconvenient legs and feet, the research of the robot target position based on monocular stereo vision and the understanding of the robot NAO are very important. Research and experiments are carried out on the target recognition and positioning in the process of NAO robot grasping. This paper proposes a recognition algorithm corresponding to quantitative component statistical information. First, extract the area of interest that contains the purpose from the image. After that, to eliminate interference in various fields and achieve target recognition, the robot cameras have almost no common field of view and can only use one camera at the same time. Therefore, this article uses the monocular vision principle to locate the target, and the detection algorithm is based on the structure of the robot head material, establishes the relationship between the height change of the machine head and the tilt angle, and improves the monocular vision NAO robot detection algorithm. According to experiments, the accuracy of the robot at close range can be controlled below 1 cm. This article completes the robot’s grasping and transmission of the target. About 80% of the external information that humans can perceive comes from vision. In addition, there are advantages such as high efficiency and good stability.

Types of screening tools used to identify potential inappropriate medication in the geriatric

Inappropriate intake of medications can increase the risk of many morbidities and mortality among the geriatric population. Therefore, assessment of drug underuse, overuse, and inappropriate use has been an area of interest across the different investigations, and according to which, different screening tools were developed to identify these problems and enhance the quality of care to these patients. In the present study, we aim comprehensively discuss the different types of currently reported screening tools that can identify potentially inappropriate medication in the geriatric population. Studies show that assess, review, minimize, optimize, reassess (ARMOR), and medication appropriateness index (MAI) tools are the most commonly reported for this purpose to appropriately evaluate drug administration practices. However, they are time-consuming and need adequately trained personnel, which might not be available within the different settings. Accordingly, we suggest that more than one tool should be used, as we have reviewed all the advantages and disadvantages of the modality within the current study, to adequately facilitate and make the process of evaluation easy and enhance the quality of care for the geriatric population.