Suchandra Dutta

We review the physics opportunities of the Future Circular Collider, covering its e+e-, pp, ep and heavy ion programmes. We describe the measurement capabilities of each FCC component, addressing the study of electroweak, Higgs and strong interactions, the top quark and flavour, as well as phenomena beyond the Standard Model. We highlight the synergy and complementarity of the different colliders, which will contribute to a uniquely coherent and ambitious research programme, providing an unmatchable combination of precision and sensitivity to new physics.

In response to the 2013 Update of the European Strategy for Particle Physics (EPPSU), the Future Circular Collider (FCC) study was launched as a world-wide international collaboration hosted by CERN. The FCC study covered an energy-frontier hadron collider (FCC-hh), a highest-luminosity high-energy lepton collider (FCC-ee), the corresponding 100 km tunnel infrastructure, as well as the physics opportunities of these two colliders, and a high-energy LHC, based on FCC-hh technology. This document constitutes the third volume of the FCC Conceptual Design Report, devoted to the hadron collider FCC-hh. It summarizes the FCC-hh physics discovery opportunities, presents the FCC-hh accelerator design, performance reach, and staged operation plan, discusses the underlying technologies, the civil engineering and technical infrastructure, and also sketches a possible implementation. Combining ingredients from the Large Hadron Collider (LHC), the high-luminosity LHC upgrade and adding novel technologies and approaches, the FCC-hh design aims at significantly extending the energy frontier to 100 TeV. Its unprecedented centre of-mass collision energy will make the FCC-hh a unique instrument to explore physics beyond the Standard Model, offering great direct sensitivity to new physics and discoveries.

The Large Hadron electron Collider (LHeC) is designed to move the field of deep inelastic scattering (DIS) to the energy and intensity frontier of particle physics. Exploiting energy recovery technology, it collides a novel, intense electron beam with a proton or ion beam from the High Luminosity--Large Hadron Collider (HL-LHC). The accelerator and interaction region are designed for concurrent electron-proton and proton-proton operation. This report represents an update of the Conceptual Design Report (CDR) of the LHeC, published in 2012. It comprises new results on parton structure of the proton and heavier nuclei, QCD dynamics, electroweak and top-quark physics. It is shown how the LHeC will open a new chapter of nuclear particle physics in extending the accessible kinematic range in lepton-nucleus scattering by several orders of magnitude. Due to enhanced luminosity, large energy and the cleanliness of the hadronic final states, the LHeC has a strong Higgs physics programme and its own discovery potential for new physics. Building on the 2012 CDR, the report represents a detailed updated design of the energy recovery electron linac (ERL) including new lattice, magnet, superconducting radio frequency technology and further components. Challenges of energy recovery are described and the lower energy, high current, 3-turn ERL facility, PERLE at Orsay, is presented which uses the LHeC characteristics serving as a development facility for the design and operation of the LHeC. An updated detector design is presented corresponding to the acceptance, resolution and calibration goals which arise from the Higgs and parton density function physics programmes. The paper also presents novel results on the Future Circular Collider in electron-hadron mode, FCC-eh, which utilises the same ERL technology to further extend the reach of DIS to even higher centre-of-mass energies.

The statistics presented by the World Health Organization attribute depression to be a primary cause of concern globally, leading to suicide in the majority of the cases if left undetected. Studies show that depression generally has an impact on the writing style and corresponding language use. The primary aim of the proposed research is to study users’ posts on Twitter and identify the attributes that may indicate depressive symptoms of online users. The paper employed machine learning approaches and natural language processing techniques for training our data and evaluating the efficiency of our proposed method. The work proposed a numerical score for each user based on the sentiment value of their tweets and demonstrated that this feature can detect depression with an accuracy of 78% with the XGBoost classifier. This attribute is combined with other Linguistic features (N-Gram+TF-IDF) and LDA to achieve an accuracy of 89% using the Support Vector Machine classifier. According to the proposed research, proper feature selection and their combinations help in achieving better improvement in performance.

In current particle physics experiments, silicon strip detectors are widely used as part of the inner tracking layers. A foreseeable large-scale application for such detectors consists of the luminosity upgrade of the Large Hadron Collider (LHC), the super-LHC or sLHC, where silicon detectors with extreme radiation hardness are required. The mission statement of the CERN RD50 Collaboration is the development of radiation-hard semiconductor devices for very high luminosity colliders. As a consequence, the aim of the R&D programme presented in this article is to develop silicon particle detectors able to operate at sLHC conditions. Research has progressed in different areas, such as defect characterisation, defect engineering and full detector systems. Recent results from these areas will be presented. This includes in particular an improved understanding of the macroscopic changes of the effective doping concentration based on identification of the individual microscopic defects, results from irradiation with a mix of different particle types as expected for the sLHC, and the observation of charge multiplication effects in heavily irradiated detectors at very high bias voltages.

The liver, the most crucial interior organ of the human body, performs functions of metabolism control and food digestion. Liver disorders can sometimes prove fatal, and appropriate treatment at the right time will save many lives. Research has been conducted to predict and diagnose liver diseases for quite a while, and Machine Learning (ML) tools have proven very effective. We have considered eight ML models for this study while working on the Liver Patient Dataset, which contains more than 30k instances, for accurately predicting liver diseases. This study includes some boosting algorithms as well. For the proper judgment of the performance of the proposed models, commonly used performance metrics such as accuracy, RoC-AuC, F1 score, precision, and recall have been used. We have inferred that the k-Nearest Neighbor (KNN) produced the most accurate results at 92.345%. Since the models are not overfitted, they are k-fold cross-validated, and hence, the standard deviation of each model is lower. Therefore, the standard deviation of KNN is 0.815, and the False Negative (FN) rate comes out to be 3.7%.

BACKGROUND Osteonecrosis or avascular necrosis (AVN) of the hip was one of the dreaded complications of coronavirus disease 2019 (COVID-19), which emerged in patients who received steroid therapy. Corticosteroids have been a mainstay in the treatment protocol of COVID-19 patients. Popular corticosteroid drugs used in patients suffering from COVID-19 were intravenous (IV) or oral dexamethasone, methylprednisolone or hydrocortisone. The use of such high doses of corticosteroids has shown very positive results and has been lifesaving in many cases. Still, long-term consequences were drug-induced diabetes, osteoporosis, Cushing syndrome, muscle wasting, peripheral fat mobilization, AVN, hirsutism, sleep disturbances and poor wound healing. A significant number of young patients were admitted for bilateral total hip replacements (THR) secondary to AVN following steroid use for COVID-19 treatment. AIM To assess the efficacy of bilateral pericapsular end nerve group (PENG) blocks in patients posted for bilateral THR post-steroid therapy after COVID-19 infection and assess the time taken to first ambulate after surgery. METHODS This prospective observational study was conducted between January 2023 and August 2023 at Care Hospitals, Hyderabad, India. Twenty young patients 30-35 years of age who underwent bilateral THR were studied after due consent over 8 months. All the patients received spinal anaesthesia for surgery and bilateral PENG blocks for postoperative analgesia. RESULTS The duration of surgery was 2.5 h on average. Seventeen out of twenty patients (85%) had a Visual Analog Score (VAS) of less than 2 and did not require any supplementation. One patient was removed from the study, as he required re-exploration. The remaining two patients had a VAS of more than 8 and received IV morphine post-operatively as a rescue analgesic drug. Fifteen out of seventeen patients (88.2%) could be mobilized 12 h after the procedure. CONCLUSION Osteonecrosis or AVN of the hip was one of the dreaded complications of COVID-19, which surfaced in patients who received steroid therapy requiring surgical intervention. Bilateral PENG block is an effective technique to provide post-operative analgesia resulting in early mobilization and enhanced recovery after surgery.

Motivated by the success of the flavour physics programme carried out over the last decade at the Large Hadron Collider (LHC), we characterize in detail the physics potential of its High-Luminosity and High-Energy upgrades in this domain of physics. We document the extraordinary breadth of the HL/HE-LHC programme enabled by a putative Upgrade II of the dedicated flavour physics experiment LHCb and the evolution of the established flavour physics role of the ATLAS and CMS general purpose experiments. We connect the dedicated flavour physics programme to studies of the top quark, Higgs boson, and direct high-$p_T$ searches for new particles and force carriers. We discuss the complementarity of their discovery potential for physics beyond the Standard Model, affirming the necessity to fully exploit the LHC's flavour physics potential throughout its upgrade eras.

According to World Health Organization statistics, depression is a prominent cause of concern worldwide, leading to suicide in the majority of these cases if left untreated. Nowadays, social media is a great place for users to express themselves through text, emoticons, images, etc., which reflect their thoughts and moods. This has opened up the possibility of studying social networks in order to better comprehend the mental states of their participants. The primary goal of the research is to examine Twitter user personas and tweets in order to uncover traits that may signal depressive symptoms among online users. A two-level depression detection method is proposed in which suspected depressed individuals are identified using social media features, personality traits, temporal and sentiment analysis of user biographies. Using the support vector machine classifier, these qualities are integrated with additional linguistic and topic features to achieve an accuracy of 89%. According to the research, effective feature selection and their combinations aid in enhancing performance.

Interstrip and backplane capacitances on silicon microstrip detectors with p+ strip on n substrate of 320μm thickness were measured for pitches between 60 and 240μm and width over pitch ratios between 0.13 and 0.5. Parametrisations of capacitance w.r.t. pitch and width were compared with data. The detectors were measured before and after being irradiated to a fluence of 4×1014protons/cm2 of 24GeV/c momentum. The effect of the crystal orientation of the silicon has been found to have a relevant influence on the surface radiation damage, favouring the choice of a 〈100〉 substrate. Working at high bias (up to 500 V in CMS) might be critical for the stability of detector, for a small width over pitch ratio. The influence of having a metal strip larger than the p+ implant has been studied and found to enhance the stability.

Abstract The CMS experiment at the CERN LHC will be upgraded to accommodate the 5-fold increase in the instantaneous luminosity expected at the High-Luminosity LHC (HL-LHC) [1]. Concomitant with this increase will be an increase in the number of interactions in each bunch crossing and a significant increase in the total ionising dose and fluence. One part of this upgrade is the replacement of the current endcap calorimeters with a high granularity sampling calorimeter equipped with silicon sensors, designed to manage the high collision rates [2]. As part of the development of this calorimeter, a series of beam tests have been conducted with different sampling configurations using prototype segmented silicon detectors. In the most recent of these tests, conducted in late 2018 at the CERN SPS, the performance of a prototype calorimeter equipped with ≈12,000 channels of silicon sensors was studied with beams of high-energy electrons, pions and muons. This paper describes the custom-built scalable data acquisition system that was built with readily available FPGA mezzanines and low-cost Raspberry Pi computers.

Social media platforms are thriving nowadays, so a huge volume of data is produced. As it includes brief and clear statements, millions of people post their thoughts on microblogging sites every day. This paper represents and analyze the capacity of diverse strategies to volumetric, delicate, and social networks to predict critical opinions from online social networking sites. In the exploration of certain searching for relevant, the thoughts of people play a crucial role. Social media becomes a good outlet since the last decades to share the opinions globally. Sentiment analysis as well as opinion mining is a tool that is used to extract the opinions or thoughts of the common public. An occurrence in one place, be it economic, political, or social, may trigger large-scale chain public reaction across many other sites in an increasingly interconnected world. This study demonstrates the evaluation of sentiment analysis techniques using social media contents and creating the association between subjectivity with herd behavior and clustering coefficient as well as tries to predict the election result (2021 election in West Bengal). This is an implementation of sentiment analysis targeted at estimating the results of an upcoming election by assessing the public's opinion across social media. This paper also has a short discussion section on the usefulness of the idea in other fields.

The Data Quality Monitoring system in the CMS experiment has been designed for the monitoring of both the online data taking as well as offline data processing, such as reconstruction of data at Tier0. The goal of the online DQM system is to monitor detector performance and identify problems efficiently and with small latency during the data collection. On the other hand the reconstruction or calibration problems can be detected during offline processing using the same tool. The monitoring is performed with histograms which are filled by accessing information from raw and reconstructed data. We shall describe the CMS Data Quality Monitoring system in general with special emphasis on its application to the monitoring of the CMS Silicon Strip Track detector. Experience with the system from the cosmic ray muon data taking, before the installation of the tracking detector in the underground CMS experiment as well as during the global runs after completion of the detector installation, will be reported.

This article describes a new charged-particle track fitting algorithm designed for use in high-speed electronics applications such as hardware-based triggers in high-energy physics experiments. Following a novel technique designed for fast electronics, the positions of the hits on the detector are transformed before being passed to a linearized track parameter fit. This transformation results in fitted track parameters with a very linear dependence on the hit positions. The approach is demonstrated in a representative detector geometry based on the CMS detector at the Large Hadron Collider. The fit is implemented in FPGA chips and optimized for track fitting throughput and obtains excellent track parameter performance. Such an algorithm is potentially useful in any high-speed track-fitting application.

The statistics presented by the World Health Organization attributes depression to be a primary cause of concern globally, leading to suicide in majority of the cases if left undetected. Nowadays, Social media is a great point for its users to express their opinions through text, emoticons, photos or videos thus reflecting their sentiments and moods. This has created an opportunity to study social network for understanding the mental state of the users. Studies show that depression generally has an impact on the writing style and corresponding language use. In addition, user persona on social media can also provide us a lot of information about the mental state of the user. The primary aim of our research is to study user’s persona and posts on Twitter and identify the attributes that may indicate depressive symptoms of online users. We used machine learning approaches and natural language processing techniques for training our data and evaluating the efficiency of our proposed method. We proposed a two-level depression detection in which the social media features, personality trait and sentiment analysis of user’ biography provide us an opportunity to identify suspected depressed users. We combined these attributes with other Linguistic features (N-Gram+TF-IDF) and LDA and achieved an accuracy of 89% using Support Vector Machine classifier. According to our research, proper feature selection and their combinations help in achieving better improvement in performance.

The paper describes the role of the n+ edge implants in the breakdown process of p+ on n-bulk silicon diodes. Laboratory measurements and simulation studies are presented on a series of test structures aimed at an optimization of the design in the edge region. The dependence of the breakdown voltage on the geometrical parameters of the devices is discussed in detail. Design rules are extracted for the use of n+-layers along the scribe line to avoid surface conduction of current generated by the exposed edges. The effect of neutron irradiation has been studied up to a fluence of 1.8×1015 cm−2.

Gas Electron Multiplier (GEM) technology is being considered for the forward muon upgrade of the CMS experiment in Phase 2 of the CERN LHC. Its first implementation is planned for the GE1/1 system in the 1.5 <| η |< 2.2 region of the muon endcap mainly to control muon level-1 trigger rates after the second long LHC shutdown. A GE1/1 triple-GEM detector is read out by 3,072 radial strips with 455 µrad pitch arranged in eight η-sectors. We assembled a full-size GE1/1 prototype of 1m length at Florida Tech and tested it in 20–120 GeV hadron beams at Fermilab using Ar/CO2 70∶30 and the RD51 scalable readout system. Four small GEM detectors with 2-D readout and an average measured azimuthal resolution of 36 µrad provided precise reference tracks. Construction of this largest GEM detector built to-date is described. Strip cluster parameters, detection efficiency, and spatial resolution are studied with position and high voltage scans. The plateau detection efficiency is [97.1 ± 0.2 (stat)]%. The azimuthal resolution is found to be [123.5 ± 1.6 (stat)] µrad when operating in the center of the efficiency plateau and using full pulse height information. The resolution can be slightly improved by ∼ 10 µrad when correcting for the bias due to discrete readout strips. The CMS upgrade design calls for readout electronics with binary hit output. When strip clusters are formed correspondingly without charge-weighting and with fixed hit thresholds, a position resolution of [136.8 ± 2.5 stat] µrad is measured, consistent with the expected resolution of strip-pitch/equation µrad. Other η-sectors of the detector show similar response and performance.

We present results obtained with full-size wedge silicon microstrip detectors bonded to APV6 (Raymond et al., Proceedings of the 3rd Workshop on Electronics for LHC Experiments, CERN/LHCC/97-60) readout chips. We used two identical modules, each consisting of two crystals bonded together. One module was irradiated with 1.7×1014neutrons/cm2. The detectors have been characterized both in the laboratory and by exposing them to a beam of minimum ionizing particles. The results obtained are a good starting point for the evaluation of the performance of the “ensemble” detector plus readout chip in a version very similar to the final production one. We detected the signal from minimum ionizing particles with a signal-to-noise ratio ranging from 9.3 for the irradiated detector up to 20.5 for the non-irradiated detector, provided the parameters of the readout chips are carefully tuned.

In our paper, we have proposed a new mechanism of E-voting using two layers of security using steganographic technique. The basic idea conveyed in our paper is simple, but novel. We have dealt with the Personal Identification Number (PIN) and the fingerprint for establishing uniqueness among the individual voters, in order to make a vote count. The techniques used here are the Least Significant Bit (LSB) embedding and the Minimal Impact Decimal Digit Embedding (MIDDE). If the steps are followed backwards, we retrieve the PIN and the fingerprint, which is impossible without prior knowledge of the embedding used. It has also been seen that our algorithm is foolproof against statistical attacks and malicious attempts of recovery.

A proposal for a pixel-based Level 1 trigger for the Super-LHC is presented. The trigger is based on fast track reconstruction using the full pixel granularity exploiting a readout which connects different layers in specific trigger towers. The trigger will implement the current CMS high level trigger functionality in a novel concept of intelligent detector. A possible layout is discussed and implications on data links are evaluated.

Abstract An essential metric for the quality of a particle-identification experiment is its statistical power to discriminate between signal and background. Pulse shape discrimination (PSD) is a basic method for this purpose in many nuclear, high-energy and rare-event search experiments where scintillation detectors are used. Conventional techniques exploit the difference between decay-times of the pulses from signal and background events or pulse signals caused by different types of radiation quanta to achieve good discrimination. However, such techniques are efficient only when the total light-emission is sufficient to get a proper pulse profile. This is only possible when adequate amount of energy is deposited from recoil of the electrons or the nuclei of the scintillator materials caused by the incident particle on the detector. But, rare-event search experiments like direct search for dark matter do not always satisfy these conditions. Hence, it becomes imperative to have a method that can deliver a very efficient discrimination in these scenarios. Neural network based machine-learning algorithms have been used for classification problems in many areas of physics especially in high-energy experiments and have given better results compared to conventional techniques. We present the results of our investigations of two network based methods viz. Dense Neural Network and Recurrent Neural Network, for pulse shape discrimination and compare the same with conventional methods.

The thyroid gland plays a significant role in the human body's metabolism, growth, and development. Though it is not a life-threatening disease, a person suffering from thyroid faces many complications in their daily life. Recent trends have shown that women suffer more from thyroid-related diseases than men. The many contributing factors that lead to thyroid disease may be controlled upon early diagnosis stages. Machine learning prediction models help healthcare professionals diagnose thyroid diseases at an initial stage and take measures accordingly. This study deployed initial Sixteen ML models, including six boosting algorithms, on a dataset of 9172 instances with related features. The model performances have been judged through various standard performance metrics. The boosting algorithms showed exceptional results, and Cat Boost (CB) model produced the best accuracy of 95.75%. The hyperparameter tuning performed on boosting models by implementing Randomized Search CV increased the accuracy to 96.19% for CB. The stacking ensemble approach was applied on top of the six boosting tuned models with the CB classifier as the meta-learner. At the same time, the other boosting algorithms were kept as a base learner for the final model prediction. The accuracy of the stack model was impressive, with 95.32% compared with default models, the ROC-AUC at 0.95, and the other results were also promising. The model’s standard deviation was significantly less at 0.57, implying the model’s stability and robustness, and the False Negative (FN) rate reached 1.8%.

This paper describes the main achievements in the development of radiation resistant silicon detectors to be used in the CMS tracker. After a general description of the basic requirements for the operation of large semiconductor systems in the LHC environment, the issue of radiation resistance is discussed in detail. Advantages and disadvantages of the different technological options are presented for comparison. Laboratory measurements and test beam data are used to check the performance of several series of prototypes fabricated by different companies. The expected performance of the final detector modules are presented together with preliminary test beam results on system prototypes.

Abstract The Special‐purpose Committee on Virtual Participation in the Nomenclature Section was established by the XIX International Botanical Congress (IBC) in Shenzhen, China in 2017, with the mandate “to investigate the possibility of and mechanisms for virtual participation and voting in the Nomenclature Section of an International Botanical Congress via the internet” and to report to the XX IBC. The wide access to the World Wide Web and availability of software for virtual meetings makes the possibility for virtual (online) attendance and voting at a Nomenclature Section seem attainable and advisable. In order to make informed recommendations, we discussed various aspects of online attendance and voting, such as: who should be able to observe?; what would qualify a person to cast institutional votes and personal votes?; if the accumulation of institutional votes should be allowed by an online voter; registration of online voters; how costs would be covered; and recommendations for online attendees. This report provides a synthesis of our discussions and is necessary for interpreting the proposals of this Special‐purpose Committee to change aspects of Div. III (Provisions for governance) of the Code (Landrum &amp; al. in Taxon 70: 1397–1398. 2021). This report and those proposals should be consulted together.

A Special-purpose Committee on Virtual Participation in the Nomenclature Section (NS) was established at the XIX International Botanical Congress in Shenzhen, China in 2017 (Wilson in Taxon 68: 160–162. 2019). The mandate of this Special-purpose Committee is “to investigate the possibility of and mechanisms for virtual participation and voting in the Nomenclature Section of an International Botanical Congress via the internet”. After discussing the concepts of virtual participation and voting, we have arrived at various suggestions, which are discussed in the accompanying report of the Committee (Landrum & al. in Taxon 70: 1399–1401. 2021). We realize that virtual or online participation in the NS would be a significant change to the International Code of nomenclature for algae, fungi, and plants (Code, Turland & al. in Regnum Veg. 159. 2018) and can only be accepted as a change after its feasibility has been proven. Therefore, we are proposing that a trial be conducted at the NS of the next International Botanical Congress. Nevertheless, we are formally proposing additions to Div. III of the Code that can be considered near the end of the NS if the trial proves successful. “4.new1. Interested individuals or groups are to be able to observe the Nomenclature Section of an International Botanical Congress online on the World Wide Web. The Organizing Committee of the International Botanical Congress in consultation with the Bureau of Nomenclature are together responsible for ensuring that this is implemented.” “4.new2. Individuals or groups without voting rights observing the Nomenclature Section online on the World Wide Web will not be charged for this service, or a small fee will be set at the discretion of the Organizing Committee of the International Botanical Congress in consultation with the Bureau of Nomenclature.” “4.new3. Registered members of the Nomenclature Section (with voting rights) attending online on the World Wide Web (online members) will pay fees similar to those that they would pay if they attended in person.” “4.new4. Individuals desiring to be online members of the Nomenclature Section will register their intention to participate in advance of the Nomenclature Section, by a date to be determined by the Organizing Committee of the International Botanical Congress in consultation with the Bureau of Nomenclature.” “5.new1. Online members of the Nomenclature Section may accumulate and cast institutional votes just as in-person members (see Prov. 5.9(b)).” “Recommendation 1. The Nomenclature Section should take place in a country and place where broadcasting the Nomenclature Section on the web is possible and allowed.” “Recommendation 2. Local groups of non-voting observers, and members (online and in-person), of the Nomenclature Section are encouraged to meet together before and during the Section to facilitate discussion of proposals, including the results of the preliminary guiding vote (see Prov. 2.5).” “Recommendation 3. When proposals or amendments to proposals are introduced during the Nomenclature Section without having been published beforehand, voting on them should be delayed, to alert members (online and in-person) who may not be present for the whole Section.” “Recommendation 4. Written recognition for participation in the Nomenclature Section should be provided to members (online and in-person) by the Organizing Committee of the International Botanical Congress.” We thank Nicholas J. Turland (B; Botanischer Garten und Botanisches Museum Berlin, Freie Universität Berlin, Germany) and John H. Wiersema (US; Smithsonian Institution, Washington, D.C., U.S.A.) for their useful comments and suggestions to improve these proposals.

The CMS experiment at the LHC will comprise a large silicon strip tracker. This article highlights some of the results obtained in the R&D studies for the optimization of its silicon sensors. Measurements of the capacitances and of the high voltage stability of the devices are presented before and after irradiation to the dose expected after the full lifetime of the tracker.

Data Quality Monitoring (DQM) is mandatory in a high energy physics experiment like the Compact Muon Solenoid (CMS) at the CERN Large Hadron Collider (LHC). For the CMS Silicon Strip Tracker, the largest detector of this kind ever built, the DQM tasks translate into monitoring of 15148 silicon detector modules for a total of 9.3 million channels. An expert level Graphical User Interface has been developed to manage this highly granular information where the statistical characteristics of more than 350000 histograms are analyzed online during data-taking in order to raise warnings or alarms. In addition, specific quantities can be recalled on demand for problems investigation or online feedback on the data quality.

Tracker Inner Barrel half-cylinders and Tracker Inner Disks of the CMS tracker have been integrated in three INFN sites. Integrated structures are submitted to an extensive set of tests whose main aim is to validate the functioning of the structures in CMS-like conditions. The tests have furthermore proven to be a great opportunity to study several aspects of the performance in detail. In this note the tests are described in some detail and an overview of the results is presented.

The identification techniques of the τ lepton in the CMS experiment based on it's hadronic decay channels are described in detail. The High Level Trigger algorithms developed in the experiment based on τ identification are also presented.

The CMS Silicon tracker consists of 70m2 of microstrip sensors which design will be finalized at the end of 1999 on the basis of systematic studies of device characteristics as function of the most important parameters. A fundamental constraint comes from the fact that the detector has to be operated in a very hostile radiation environment with full efficiency. We present an overview of the current results and prospects for converging on a final set of parameters for the silicon tracker sensors.

The paper presents the results of an extensive set of measurements performed on silicon microstrip sensors produced by STMicroelectronics for the CMS Tracker. 5″ and 6″ technologies were used to process several series of detector prototypes. Detectors 300μm thick were produced on 5″ wafers and fully characterized. A new design on 500μm thick wafers with 6″ technology has been recently implemented. The performance of three different layouts has been investigated in terms of macroscopic electrical parameters and radiation resistance.

The devastating outbreak of the SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus) also known as COVID-2019 has succeeded in introducing the danger to the worldwide living. Now COVID-19 is among the main potentially deadly issues in the world. Rapid and precise identification of COVID-19 infection is important to diagnose, make informed assumptions, and assure that patients receive care that aims to save people's health and life. The entire community is making enormous endeavors to tackle the spreading of such a horrible epidemic in forms of communications true, economy, information sources, safety equipment, existence-risk treatments, and many on this and many other tools. Coronavirus triggers a vast range of viral disease; however, it is a virus of the kind RNA which can affect all humans and animals. Coronavirus is now identified in this chapter uses a form of deep learning which is a sub-branch of artificial intelligence. This chapter suggests making use of the deep learning algorithms with such a view to recognizing its daily incremental behavior and predicting the potential accessibility of COVID-2019 throughout civilizations using real-time data knowledge. COVID-19 Quick diagnosis and It is necessary to identify high-risk patients with the worst diagnosis for early treatment and optimization of medical services. And the algorithm of deep learning helps to do that and helps to combat the COVID-19 and the deep learning algorithms are more time saving; less expensive; easy to operate. COVID-19 research through deep learning involves the patient's x-rays of the lungs and the fundamental concept is to identify the ultrasound as impaired or usual COVID. In general, the issue is a collection of identification algorithms whereby we identify Standard normal v/s COVID-19 cases. There are several benefits and drawbacks to utilizing Deep Learning to solve these circumstances.

As the very first big outbreak in our era, COVID-19 offers an outstanding incentive to legislators, and regulatory authorities should find procedural viability, moral validity, and usefulness of implementation in the time of new healthcare technologies. This chapter addresses the outbreak of coronavirus and how digital healthcare technologies can help navigate and resolve COVID-19 and future epidemics. As the coronavirus pandemic (COVID-2019) spreads, technological advancements and efforts to control the disease, manage clients safely, and promote teamwork among overburdened health care practitioners designing innovative, efficient vaccines would be critical. This review looks at how various healthcare scientific sectors are assisting in the fight against the pandemic, as well as how innovative implementations are being used to treat or cure illness. An analysis of the technical landscape in the COVID-19 sense enables some tentative reflections about the conditions of technical involvement in fighting this once-in-a-century pandemic. Firstly, unlike previous global health crises, it seems to turn people from monitoring artefacts and epidemiological analysis through information creation topics via self-tracking, information sharing, and digital information flows. Furthermore, even if many of such innovations were not implemented, in a previous medical crisis background, their extensive usage on a world basis raises concern about the extent of mobilising domestic spying techniques on racial equality and also worries about government agencies retaining higher monitoring rates long after the end of the pandemic. Within the recent epidemic framework, advanced technical technologies in data analysis and remote monitoring of the urgent laws that require the immediate termination of human rights and the approval of medical equipment and vaccinations by fast-tracking processes have been implemented. Everyone knows that digital healthcare technologies are progressively being embraced in this field for fighting against the COVID-19 pandemic. This chapter produced an overview to explain all the efforts being made towards this pandemic in digital healthcare tech. Healthcare centres throughout the world are transitioning to the new technologies listed below to help ease their workload, either by increasing the speed diagnostics or allowing physicians to monitor patients who have been virtually quarantined.

Technological advancement in computational areas has already entered to every fields of science, sociology, health, business, and in many others. Now people are connected across the world using Internet and more precisely it can be said that the communication is done freely through online social networks. From that point of view, researchers are trying to analyze the social networks for the betterment of different domains or trying to create some useful applications through social networks. Now aim is to use this ground to help the people by using the information of crime sufferers to reduce the criminal activities of future. It will benefit the people to get some ideas about the different criminal activities and who are the victims and how they have been victimized. Here, the open-source, GUI-based machine learning tool, WEKA, has been used to analyze the collected data. And last but not the least this process will be helpful for recommendation system and herd behavior analysis—two major aspects of social network analysis and data mining.

For the construction of the silicon microstrip detectors for the Tracker of the CMS experiment, two different substrate choices were investigated: A high-resistivity (6 k cm) substrate with (111) crystalorientation and a low-resistivity (2k cm) one with (100) crystalorientation. The interstrip and backplane capacitances were measured before and after the exposure to radiation in a range of strip pitches from 60 μm to 240 μm and for values of the width-over-pitch ratio between 0.1 and 0.5.

The breakdown performance of CMS barrelmodule prototype detectors and test devices with single and multi-guard structures were studied before and after neutron irradiation up to 2·1014 1 MeV equivalent neutrons. Before irradiation avalanche breakdown occurred at the guard ring implant edges. We measured 100–300 V higher breakdown voltage values for the devices with multi-guard than for devices with single-guard ring. After irradiation and type inversion the breakdown was smoother than before irradiation and the breakdown voltage value increased to 500–600 V for most of the devices.

TAXONVolume 64, Issue 6 p. 1341-1341 Proposals to Amend the CodesFree Access (111) Proposal to amend Recommendation 40A.3 Suchandra Dutta, Corresponding Author Suchandra Dutta suchandra.dutta@gmail.com Department of Botany, R. D. & S. H. National College, Bandra (W), 400050 Mumbai, IndiaSearch for more papers by this authorK. M. Manudev, K. M. Manudev Department of Botany, St. Joseph’s College, Devagiri, 673008 Kozhikode Kerala, IndiaSearch for more papers by this author Suchandra Dutta, Corresponding Author Suchandra Dutta suchandra.dutta@gmail.com Department of Botany, R. D. & S. H. National College, Bandra (W), 400050 Mumbai, IndiaSearch for more papers by this authorK. M. Manudev, K. M. Manudev Department of Botany, St. Joseph’s College, Devagiri, 673008 Kozhikode Kerala, IndiaSearch for more papers by this author First published: 11 December 2015 https://doi.org/10.12705/646.38AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume64, Issue6December 2015Pages 1341-1341 RelatedInformation

The decision taken by the CMS experiment to build a tracker entirely based on silicon detectors has made necessary the use of thicker sensors instead of the usual 300-/spl mu/m sensors for the outer part of the detector. We first present results on the performance of 500-/spl mu/m-thick detectors, before and after neutron irradiation, bonded to the CMS tracker front-end electronics. Laboratory measurements show that the total collected charge scales linearly with thickness when compared with a 300-/spl mu/m module, and the measured noise is in good agreement with expectations. The results obtained confirm the feasibility of large-area silicon trackers.

The Data Quality Monitoring system for the Tracker has been developed within the CMS Software framework. It has been designed to be used during online data taking as well as during offline reconstruction. The main goal of the online system is to monitor detector performance and identify problems very efficiently during data collection so that proper actions can be taken to fix it. On the other hand any issue with data reconstruction or calibration can be detected during offline processing using the same tool. The monitoring is performed using histograms which are filled with information from raw and reconstructed data computed at the level of individual detectors. Furthermore, statistical tests are performed on these histograms to check the quality and flags are generated automatically. Results are visualized with web based graphical user interfaces. Final data certification is done combining these automatic flags and manual inspection. The Tracker DQM system has been successfully used during cosmic data taking and it has been optimised to fulfill the condition of collision data taking. In this paper we describe the functionality of the CMS Tracker DQM system and the experience acquired during proton-proton collision.

This paper investigates the performance of 500μm thick silicon microstrip detectors before and after heavy irradiation. Prototype sensors, produced by STMicroelectronics, have been extensively studied using laboratory measurements, a radioactive source and a beam of minimum ionising particles. The comparison with a standard 300μm sensor shows that the collected charge in thick devices scales linearly with thickness. By over-depleting the irradiated devices, the pre-irradiated charge collection efficiency is fully recovered. The measured noise is in good agreement with expectations. Although more work is needed, the paper shows that 500μm thick devices are a promising technology for very large tracking systems.

The data mining methods are often beneficial in open domains like business, marketing, and retail. Healthcare is one of these areas that are still in its development phase. The healthcare industry is very rich in information, but sadly, not all data is carefully mined or discovered to draw out the effective, hidden patterns, and due to the lack of helpful analysis tools for discovering hidden relationships and trends in data, decision making is hampered (Dangare and Apte in Int J Comput Appl (0975–888) 47(10), 2012 [1]). There are a lot of advanced techniques in determining the domain that is used to discover knowledge from the healthcare database, particularly in diseases like heart disease, lung cancer, Parkinson’s disease, and others. This paper analyzes the predictions of heart disease on a dataset with 13 attributes. On the heart disease dataset, decision trees, Naive Bayes, support vector machine, linear discriminant analysis, logistic regression, and KNN are evaluated as data mining approaches (classification algorithms). Usually, the algorithms are compared based on their performance measure, and the algorithm with the highest accuracy is implemented on the heart dataset for the further model prediction.

In this current era, sentiment analysis and also recommendation systems are some of the most popular subjects for the majority of the researchers, and on the other hand, these are the most important things for our regular life. The aim is to make valid recommendations and the most relevant tweets to the user of this model by extracting and analyzing random tweets based on a particular user address or hashtag. Nowadays, people value time over money, so we aim to save the user his or her most precious time and recommend the pertinent tweets. This model will filter out all the irrelevant as well as the inappropriate tweets and try to provide the gravest and valued tweets from Twitter. This filtration will happen based on the tweet’s public features as well as the attributes of the respective tweeter such as his or her follower count. The most initial concepts of machine learning, recommendation system, and sentiment analysis are understood and implemented to be able to propose the mentioned model. The tools to work with the mentioned topics are implemented to evaluate prominent output with utmost effort throughout. Let alone being used in product recommendation systems, our proposed method is also capable enough to maintain a consistent and effective performance in other fields.

Abstract We present a flexible and scalable approach to address the challenges of charged particle track reconstruction in real-time event filters (Level-1 triggers) in collider physics experiments. The method described here is based on a full-mesh architecture for data distribution and relies on the Associative Memory approach to implement a pattern recognition algorithm that quickly identifies and organizes hits associated to trajectories of particles originating from particle collisions. We describe a successful implementation of a demonstration system composed of several innovative hardware and algorithmic elements. The implementation of a full-size system relies on the assumption that an Associative Memory device with the sufficient pattern density becomes available in the future, either through a dedicated ASIC or a modern FPGA. We demonstrate excellent performance in terms of track reconstruction efficiency, purity, momentum resolution, and processing time measured with data from a simulated LHC-like tracking detector.

An important tool for the discovery of new physics at LHC is the design of a low level trigger with an high power of background rejection. The contribution of pixel detector to the lowest level trigger at CMS is studied focusing on low-energy jet identification, matching the information from calorimeters and pixel detector. In addition, primary vertex algorithms are investigated. The performances are evaluated in terms of, respectively, QCD rejection and multihadronic jets final states efficiency.

$$B^{0}_{s} \rightarrow \phi \phi \rightarrow $$ KKKK is an important physics process to study at the High LuminosityHigh luminosity Large Hadron Collider (LHC), also known as LHC Phase II, which is expected to start in 2024–25. The process involves a CP violating weak phase which arises due to CP violation in $$B^{0}_{s} \,-\, bar{B}^0_{s}$$ mixing through the rare gluonic penguin decay, b $$\rightarrow $$ s $$\bar{s}$$ s. The CMS experiment will install a new tracking system for the LHC Phase II and include tracker information at Level-1 trigger to keep the event rate manageable without sacrificing any physics potential. The aim of the present study is to understand whether the CMS detector will be able to trigger on the fully hadronic $$B^{0}_{s} \rightarrow \phi \phi \rightarrow $$ KKKK events and complement the LHCb experiment.

Bound and resonance states of symmetric three-body exotic \(pXX\) negative atomic ions \((X=\mu^{-}, \pi^{-}, K^{-})\) as well as exotic \(ppX\) positive molecular ions for total angular momentum \(J=0\), are studied in details under the framework of Stabilization method. The resonance states under consideration lie below \(N=2\) ionization threshold of the corresponding \(pX\) atom. The wave-function is expanded in correlated multi-exponent Hylleraas type basis set for explicit incorporation of \(p\)-\(p\), \(\mu\)-\(\mu\), \(\pi\)-\(\pi\) or \(K\)-\(K\) correlations. The methodology has been tested by estimating the parameters of the resonance states of \((p\mu\mu)^{-}\), \((pp\mu)^{+}\), \((p\pi\pi)^{-}\) and \((pp\pi)^{+}\) and comparing with the results existing in the literature. The interparticle interactions for all the systems under consideration are purely Coulombic.

As part of its HL-LHC upgrade program, the CMS Collaboration is developing a High Granularity Calorimeter (CE) to replace the existing endcap calorimeters. The CE is a sampling calorimeter with unprecedented transverse and longitudinal readout for both electromagnetic (CE-E) and hadronic (CE-H) compartments. The calorimeter will be built with $\sim$30,000 hexagonal silicon modules. Prototype modules have been constructed with 6-inch hexagonal silicon sensors with cell areas of 1.1~$cm^2$, and the SKIROC2-CMS readout ASIC. Beam tests of different sampling configurations were conducted with the prototype modules at DESY and CERN in 2017 and 2018. This paper describes the construction and commissioning of the CE calorimeter prototype, the silicon modules used in the construction, their basic performance, and the methods used for their calibration.

The aim of this paper is to investigate the Graph Based Anomaly Detection (GBAD) systems to find anomalies or features in a graph that are inconsistent with the general or maximal substructures of the graph. A substructure miner approach was implemented. The Frequent Substructure Miner (FSM) was adopted to find the optimal substructure, which was then used to compare the normal GBAD and Minimum Description Length (MDL) approach that has been in use. The FSM approach uses graphs of size 10, 100 and 1000 nodes to determine the resulting efficiency and hence the runtime as well. The runtime determines how long the two systems require to find anomalies in each type of graph.

Hibiscus hirtus L. var.inarticulatus (Malvaceae), a new variety is described and illustrated from India.It differs from the typical form and H. hirtus var.talbotii in having 3-lobed lower leaves, lanceolate upper leaves and in the absence of articulation in the pedicels.

Chinese Centre for Disease Control and Prevention announced a novel coronavirus as causative pathogen of COVID-19 on 08th January 2020, after the epidemic broke in Wuhan, China. The coronavirus infection has been incontrollable since then and was given the status of pandemic by the World Health organization in March 2020. World has come to a standstill with the advent of COVID-19 pandemic and multiple changes have made way in the normal functioning of life as well as health-care system. Despite lockdown and restrictions on movement and travel, the virus has managed to seep into multiple countries and presently India is witnessing more than 50,000 new cases daily. Health-care system all over world is exhausted and struggling to handle this contagious virus. This article is an attempt to summarize essential knowledge of COVID-19, review current understanding of COVID-19, and provide recommended management protocols for functioning of operation theater complex which have been formulated and are being followed in our Armed Forces Zonal Hospital in Southern India since Mar 2020.

In today's tech-savvy world every industry is trying to formulate methods for recommending products by combining several techniques and algorithms to form a pool that would bring forward the most enhanced models for making the predictions. Building on these lines is our paper focused on the application of sentiment analysis for recommendation in the insurance domain. We tried building the following Machine Learning models namely, Logistic Regression, Multinomial Naive Bayes, and the mighty Random Forest for analyzing the polarity of a given feedback line given by a customer. Then we used this polarity along with other attributes like Age, Gender, Locality, Income, and the list of other products already purchased by our existing customers as input for our recommendation model. Then we matched the polarity score along with the user's profiles and generated the list of insurance products to be recommended in descending order. Despite our model's simplicity and the lack of the key data sets, the results seemed very logical and realistic. So, by developing the model with more enhanced methods and with access to better and true data gathered from an insurance industry may be the sector could be very well benefitted from the amalgamation of sentiment analysis with a recommendation.

During current COVID-19 pandemic situation where preventive and curative medicines are unavailable, strong immune system is one of the best weapons to deal with it at individual level. Functional foods and dietary modification can decrease any kind of infections (viral as well as other infections) risk including COVID-19 and build up our immunity. Persons with strong immunity are able to recover any infectious disease conditions within short period of time. Main concern of this content is to support the principle that flax seed, a plant-derived omega-3 (ù-3) polyunsaturated fatty acids (rich in α-linolenic acid) can activate both adaptive and innate immune functions, altered macrophage function through production and secretion of cytokines and chemokines, improve phagocytosis capacity leads to increase immunity. Two bioactive compounds lignans and fiber are also present in flax seed. Flax seed reduces inflammation oxidative stress, and shows lipid modulating properties also. Omega -3 fatty acids popular for its health benefits against comorbidities in the COVID-19 patients including hypertension, respiratory system disease and cardiovascular disease, diabetes, cancer. Flax seed helps to maintain intestinal environment, function and immunity through modulating the gut microbiota composition, improve intestinal wall integrity, production of short chain fatty acids (due to fermentation of flaxseed fiber by gut microbiota). However it is established 92that the inclusion of sufficient amount of flax seed as whole, powder, oil, or fortified form in our regular diet helps to boost immunity against infectious disease.

Article 31.1 of the Shenzhen Code (Turland & al. in Regnum Veg. 159. 2018) states: “The date of effective publication is the date on which the printed matter or electronic material became available as defined in Art. 29 and 30. In the absence of proof establishing some other date, the one appearing in the printed matter or electronic material must be accepted as correct.” In the case of printed matter that is not already published as electronic material, there is often a gap of time between the date that appears in the printed matter and the actual distribution of the printed matter. To eliminate this gap of time, we are proposing to amend Rec. 31B.1, also incorporating Rec. 31B.2 and thereby making Rec. 31B more concise. “31B.1. The date of effective publication should be clearly indicated as precisely as possible (day, month, year) within a publication the printed matter or electronic material. In printed matter not already published as electronic material, the date should conform to Rec. 31A.1. When a publication is issued in parts, this date should be indicated in each part.” “31B.2. In electronic material, the precise dates (year, month, and day) of effective publication should be included.” We thank Nicholas J. Turland and Dr. John H. Wiersema for their helpful suggestions and refining the manuscript.

The Charge Collection Efficiency (CCE) for heavily irradiated silicon devices has been carefully investigated on a series of microstrip detectors. Large-area sensors designed for the CMS silicon tracker have been irradiated with neutrons and protons up to a very high fluence. Effects on CCE have been studied using a beam of minimum ionizing particles and a fast shaping time electronics similar to what is expected in CMS. The paper shows the performance of the sensors for CCE and Signal-to-Noise ratio (S/N) under different operating conditions.

We report selected results of laboratory measurements and beam tests of heavily irradiated microstrip silicon detectors. The detectors were single-sided devices, produced by different manufacturers and irradiated with different sources, for several total ionizing doses and fluences up to 4 /spl times/10/sup 14/ 1-MeV-equivalent neutrons per cm/sup 2/. Strip resistance and capacitance, detector leakage currents and breakdown performance were measured before and after irradiations. Signal-to-noise ratio and detector efficiency were studied in beam tests, for different values of the detector temperature and of the read-out pitch, as a function of the detector bias voltage. The goal of these test is to optimise the design of the final prototypes for the Silicon Strip Tracker of the CMS experiment at the CERN LHC collider.

This paper investigates the performance of silicon microstrip detectors after heavy irradiation. Full-size prototype sensors (53 /spl times/ 64 mm/sup 2/) designed for the CMS Tracker have been irradiated with protons and extensively studied in the laboratory and using a beam of minimum ionising particles operated at low temperature as foreseen for the Large Hadron Collider. We present results of large statistics measurements of collected charge, noise, position resolution, and hit finding efficiency for these irradiated detectors.

The paper reports the results of a joint R&D project between INFN Pisa and STMicroelectronics aiming at the development of silicon micro-strip detectors with very high breakdown voltage. Several series of prototypes have been manufactured on 6-in-diameter n-type silicon wafers. The production technology was tuned for the standard high volume production lines and was optimized to reach a high processing yield while maintaining very good detector performance. We present a complete characterization of the devices in terms of leakage current, depletion voltage, quality and uniformity of coupling capacitors and polysilicon resistors. We discuss the main design rules and the most important technological steps which led to breakdown voltage systematically exceeding 1000 V even for very large area detectors.

The decision-taken by the CMS experiment to build a tracker entirely based on silicon detectors has made necessary the use of thicker sensors instead of the usual 300 /spl mu/m ones for the outer part of the detector. We present first results on the performance of 500 /spl mu/m thick detectors, before and after neutron irradiation, bonded to the CMS tracker front-end electronics. Laboratory measurements show that the total collected charge scales linearly with thickness when compared with a 300 /spl mu/m module and the measured noise is in good agreement with expectations. The results obtained confirm the feasibility of large area silicon trackers.

The CMS silicon strip tracker involves about 70 m2 of instrumented silicon, with approximately 18500 microstrip detectors read out by 5 × 106 electronics channels. It has to satisfy a set of stringent requirements imposed by the environment and by the physics expected at the LHC: low cell occupancy and good resolution, radiation hardness aided by adequate cooling, low mass combined with high stability. These conditions have been incorporated in a highly modular design of the detector modules and their support structures, chosen to facilitate construction and to allow for easy assembly and maintenance.

The performance of silicon micro-strip detectors after heavy irradiationhave beeninvestigated using a muonbeam. Large-area sensors have been irradiated with neutrons and protons, read-out with fast shaping time electronics, and operated at low temperature. The paper presents a study of the charge collection efficiency, signal-to-noise ratio and hit reconstruction efficiency of these silicon devices.

This paper describes the silicon microstrip tracker of the CMS experiment at the future LHC. The silicon tracker consists of a barrel part with 5 layers and two endcaps with 10 disks each. About 6500 modules will have to be built, each one carrying two daisy-chained silicon sensors and their front-end electronics. The modules have been designed to be as simple and robust as possible. Radiation damage in the silicon sensors is minimized by cooling the whole system down to -10°C. Safe operation after heavy irradiation will be possible due to the high-voltage capability of the sensors. We expect the sensors to have a signal-to-noise ratio of 10 at the end of 10years of LHC running, which still gives an efficiency of almost 100%.