Suppression of Higgsino mediated proton decay by cancellations in GUTs and strings

A mechanism for the enhancement for proton lifetime in supersymmetric/supergravity (SUSY/SUGRA) grand unified theories (GUTs) and in string theory models is discussed where Higgsino mediated proton decay arising from color triplets (anti-triplets) with charges $Q=-1/3(1/3)$ and $Q=-4/3(4/3)$ is suppressed by an internal cancellation due to contributions from different sources. We exhibit the mechanism for an SU(5) model with $45_H+\bar{45}_H$ Higgs multiplets in addition to the usual Higgs structure of the minimal model. This model contains both $Q=-1/3(1/3)$ and $Q=-4/3(4/3)$ Higgs color triplets (anti-triplets) and simple constraints allow for a complete suppression of Higgsino mediated proton decay. Suppression of proton decay in an SU(5) model with Planck scale contributions is also considered. The suppression mechanism is then exhibited for an SO(10) model with a unified Higgs structure involving $144_H+\bar{144}_H$ representations.The SU(5) decomposition of $144_H+\bar{144}_H$ contains $5_H+\bar 5_H$ and $45_H+\bar{45}_H$ and the cancellation mechanism arises among these contributions which mirrror the SU(5) case. The cancellation mechanism appears to be more generally valid for a larger class of unification models. Specifically the cancellation mechanism may play a role in string model constructions to suppress proton decay from dimension five operators. The mechanism allows for the suppression of proton decay consistent with current data allowing for the possibility that proton decay may be visible in the next round of nucleon stability experiment.Comment: 26 pages, no figures. Revtex 4. To appear in Physical Review

catena-Poly[[diaqua­calcium]bis­[μ-2-(1,3-dioxoisoindolin-2-yl)acetato]-κ3 O,O′:O;κ3 O:O,O′]

In the title complex, [Ca(C10H6NO4)2(H2O)2]n, the CaII atom lies on a twofold rotation axis and adopts a dodeca­hedral geometry. The CaII atom is octa­coordinated by two O atoms from two water mol­ecules and six O atoms from four acetate ligands. Each acetate acts as a tridentate ligand bridging two CaII atoms, resulting in a chain running along the c axis. O—H⋯O and C—H⋯O hydrogen bonds connect the chains into a two-dimensional network parallel to [011]. π–π inter­actions between adjacent isoindoline-1,3-dione rings [centroid–centroid distance = 3.4096 (11) Å] further consolidate the structure. One of the carboxylate O atoms is disordered over two sites in a 0.879 (12):0.121 (12) ratio

The 1:1 adduct of caffeine and 2-(1,3-dioxoisoindolin-2-yl)acetic acid

In the crystal structure of the title adduct [systematic name: 2-(1,3-dioxoisoindolin-2-yl)acetic acid–1,3,7-trimethyl-1,2,3,6-tetra­hydro-7H-purine-2,6-dione (1/1)], C8H10N4O2·C10H7NO4, the components are linked by an O—H⋯N hydrogen-bond and no proton transfer occurs

Early complications after biliary enteric anastomosis for benign diseases: A retrospective analysis

Background:Biliary-enteric anastomosis (BEA) is a common surgical procedure performed for the management of biliary obstruction or leakage that results from a variety of benign and malignant diseases. Complications following BEA are not rare. We aimed to determine the incidence and the factors associated with early complications occurring after BEA for benign diseases. Methods: We reviewed the medical records of all Patients who underwent BEA for benign diseases at our institution between January 1988 and December 2009. The primary outcome was early post operative complication. Logistic regression analysis was done to identify factors predicting the occurrence of complications. Results: Records of 79 Patients were reviewed. There were 34 (43%) males and 45 (57% females). Majority (53%) had choledocholithiasis with impacted stone or distal stricture, followed by traumatic injury to the biliary system (33%). Thirty-four Patients (43%) underwent a hepaticojejunostomy, 19 Patients (24%) underwent a choledochojejunostomy, and choledochoduodenostomy was performed in 26 Patients (33%). Early complications occurred in 39 (49%) Patients - 41% had local complications and 25% had systemic complications. Most frequent complications were wound infection (23%) and bile leak (10%). Four (5%) Patients died. On multivariate analysis, low serum albumin level (odds ratio = 16, 95% CI = 1.14-234.6) and higher ASA levels (odds ratio = 7, 95% CI: 1.22-33.34) were the independent factors predicting the early complications following BEA. Conclusions: Half of the Patients who underwent BEA for benign diseases had complications in our population. This high incidence may be explained by the high incidence of hypoalbuminemia and the high-risk group who underwent operation

Socio – Political Context and Inferences from Remote Sensing in South Asia: A Study of Tectonic Induced Surface Deformation in SE-IKSZ

Natural or man‐made disasters are dreadful incidents that devastate lives, disturb the socioeconomic and socio-political structure of a society and preserve or erase developments and gains based on decades, within few minutes. A catastrophe has the capacity to affect existing general population to their base, parting an occasion for self-investigation and reassessment of their framework and composition. This study signifies the Radar Digital Elevation Model centered pattern of drainage network to appraise the catastrophic landslide events due to the 2005 earthquake in Neelum-Jhelum Valley in SE-Indus Kohistan Zone north of Pakistan. This investigation highlights zones affected by the earthquake and vulnerable to landslides by utilizing Hypsometric integrals (HI values) and Hack SL-gradient techniques that are proficient in detecting erosion, land mass and tectonic movements. Dataset principally includes “Shuttle Radar Topography Mission (SRTM)” Digital Elevation Model having pixel resolution of 90 meters. Hypsometric investigation brings evidence related to the deformation periods of a geographical stage. To accomplish this objective, D8 method was used, 355 subbasins of 4th Strahler order, from 5th Strahler order 75 subbasins and from 6th Strahler order 15 subbasins were delineated. To appraise the indentations of erosional scarps, Hypsometric curves (HC) and Hypsometric integrals (HI) for all distinct subbasins were computed. Variable topographic elevations (Maximun, minimum and mean) were determined to decipher the HI values. The HCs are characterized as convex up, S shaped and concave down curves. Curvature of convex up symbolizes a lesser amount of eroded or deformed subbasins (comparatively young geography), and are located in conjunction of the North-Eastern anticline side of the Muzaffarabad that indicates the tectonic behavior of HKS, however S-shaped curvatures denote the transitional stage between the convex up and concave down deformational stage. The curvatures of concave down represents extreme deformation phase and are located west and eastern side of HKS

Improving Machine Learning Classification Accuracy for Breathing Abnormalities by Enhancing Dataset

The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as coronavirus disease (COVID)-19, has appeared as a global pandemic with a high mortality rate. The main complication of COVID-19 is rapid respirational deterioration, which may cause life-threatening pneumonia conditions. Global healthcare systems are currently facing a scarcity of resources to assist critical patients simultaneously. Indeed, non-critical patients are mostly advised to self-isolate or quarantine themselves at home. However, there are limited healthcare services available during self-isolation at home. According to research, nearly 20–30% of COVID patients require hospitalization, while almost 5–12% of patients may require intensive care due to severe health conditions. This pandemic requires global healthcare systems that are intelligent, secure, and reliable. Tremendous efforts have been made already to develop non-contact sensing technologies for the diagnosis of COVID-19. The most significant early indication of COVID-19 is rapid and abnormal breathing. In this research work, RF-based technology is used to collect real-time breathing abnormalities data. Subsequently, based on this data, a large dataset of simulated breathing abnormalities is generated using the curve fitting technique for developing a machine learning (ML) classification model. The advantages of generating simulated breathing abnormalities data are two-fold; it will help counter the daunting and time-consuming task of real-time data collection and improve the ML model accuracy. Several ML algorithms are exploited to classify eight breathing abnormalities: eupnea, bradypnea, tachypnea, Biot, sighing, Kussmaul, Cheyne–Stokes, and central sleep apnea (CSA). The performance of ML algorithms is evaluated based on accuracy, prediction speed, and training time for real-time breathing data and simulated breathing data. The results show that the proposed platform for real-time data classifies breathing patterns with a maximum accuracy of 97.5%, whereas by introducing simulated breathing data, the accuracy increases up to 99.3%. This work has a notable medical impact, as the introduced method mitigates the challenge of data collection to build a realistic model of a large dataset during the pandemic

RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices

Non-contact detection of the breathing patterns in a remote and unobtrusive manner has significant value to healthcare applications and disease diagnosis, such as in COVID-19 infection prediction. During the epidemic prevention and control period of COVID-19, non-contact approaches have great significance because they minimize the physical burden on the patient and have the least requirement of active cooperation of the infected individual. During the pandemic, these non-contact approaches also reduce environmental constraints and remove the need for extra preparations. According to the latest medical research, the breathing pattern of a person infected with COVID-19 is unlike the breathing associated with flu and the common cold. One noteworthy symptom that occurs in COVID-19 is an abnormal breathing rate; individuals infected with COVID-19 have more rapid breathing. This requires continuous real-time detection of breathing patterns, which can be helpful in the prediction, diagnosis, and screening for people infected with COVID-19. In this research work, software-defined radio (SDR)-based radio frequency (RF) sensing techniques and machine learning (ML) algorithms are exploited to develop a platform for the detection and classification of different abnormal breathing patterns. ML algorithms are used for classification purposes, and their performance is evaluated on the basis of accuracy, prediction speed, and training time. The results show that this platform can detect and classify breathing patterns with a maximum accuracy of 99.4% through a complex tree algorithm. This research has a significant clinical impact because this platform can also be deployed for practical use in pandemic and non-pandemic situations

Development of an Intelligent Real-time Multi-Person Respiratory Illnesses Sensing System using SDR Technology

Respiration monitoring plays a vital role in human health monitoring, as it is an essential indicator of vital signs. Respiration monitoring can help determine the physiological state of the human body and provide insight into certain illnesses. Recently, non-contact respiratory illness sensing methods have drawn much attention due to user acceptance and great potential for real-world deployment. Such methods can reduce stress on healthcare facilities by providing modern digital health technologies. This digital revolution in the healthcare sector will provide inexpensive and unobstructed solutions. Non-contact respiratory illness sensing is effective as it does not require users to carry devices and avoids privacy concerns. The primary objective of this research work is to develop a system for continuous real-time sensing of respiratory illnesses. In this research work, the non-contact software-defined radio (SDR) based RF technique is exploited for respiratory illness sensing. The developed system measures respiratory activity imprints on channel state information (CSI). For this purpose, an orthogonal frequency division multiplexing (OFDM) transceiver is designed, and the developed system is tested for single-person and multi-person cases. Nine respiratory illnesses are detected and classified using machine learning algorithms (ML) with maximum accuracy of 99.7% for a single-person case. Three respiratory illnesses are detected and classified with a maximum accuracy of 93.5% and 88.4% for two- and three-person cases, respectively. The research provides an intelligent, accurate, continuous, and real-time solution for respiratory illness sensing. Furthermore, the developed system can also be deployed in office and home environments

Contactless Small-Scale Movement Monitoring System Using Software Defined Radio for Early Diagnosis of COVID-19

The exponential growth of the novel coronavirus disease (N-COVID-19) has affected millions of people already and it is obvious that this crisis is global. This situation has enforced scientific researchers to gather their efforts to contain the virus. In this pandemic situation, health monitoring and human movements are getting significant consideration in the field of healthcare and as a result, it has emerged as a key area of interest in recent times. This requires a contactless sensing platform for detection of COVID-19 symptoms along with containment of virus spread by limiting and monitoring human movements. In this paper, a platform is proposed for the detection of COVID-19 symptoms like irregular breathing and coughing in addition to monitoring human movements using Software Defined Radio (SDR) technology. This platform uses Channel Frequency Response (CFR) to record the minute changes in Orthogonal Frequency Division Multiplexing (OFDM) subcarriers due to any human motion over the wireless channel. In this initial research, the capabilities of the platform are analyzed by detecting hand movement, coughing, and breathing. This platform faithfully captures normal, slow, and fast breathing at a rate of 20, 10, and 28 breaths per minute respectively using different methods such as zero-cross detection, peak detection, and Fourier transformation. The results show that all three methods successfully record breathing rate. The proposed platform is portable, flexible, and has multifunctional capabilities. This platform can be exploited for other human body movements and health abnormalities by further classification using artificial intelligence