I exported vplan to .odf file in vManager and after editing it I imported it to vManager. The vplan was expected to be synchronized and updated. However, nothing has changed to it. Does anyone know why?

I would like to send an update about a vmanager regression status x days after the regression has been run. In the current environment, the vmanager regression is creating a new filepath for logs automatically based on regression name/date, so I can't use a cron job to gather logs, as the log location is not known. 

I tried to use the pre session script to launch a detached shell script that would run after a delay, but when the pre_script runs, it waits until everything is completed before finishing and moving on to starting the regression.

Here is the test pre_script I am using:

#!/bin/sh

echo "pre_script start"

delay_script "FIRST" 1
nohup delay_script "SECOND" 30 & disown
delay_script "THIRD" 1

echo "pre_script end"
exit 0

Here is the test delay_script I am using:

#!/bin/sh

echo "Starting $1"

sleep $2

echo "Ending $1"

Here is the script output when run from terminal. After the "pre_script end", I get control back.

Here is the script output when run from vmanager. There is no "nohup", and the pre_session phase doesn't complete until all the delay scripts complete.

My question is, is there a better way to achieve my goal here? (The goal being to run a script from the vmanager log directory automatically x days after the regression). I think I could use the pre_script to send directory information for an auxiliary cron job to pick up, but I would prefer to not have to have extra cronjobs needed for this.

When analyzing multiple sessions simultaneously Verisium Manager crashed and reported below error messages:

# A fatal error has been detected by the Java Runtime Environment:
#
#  SIGSEGV (0xb) at pc=0x00007efc52861b74, pid=14182, tid=18380
#
# JRE version: OpenJDK Runtime Environment Temurin-17.0.3+7 (17.0.3+7) (build 17.0.3+7)
# Java VM: OpenJDK 64-Bit Server VM Temurin-17.0.3+7 (17.0.3+7, mixed mode, sharing, tiered, compressed oops, compressed class ptrs, g1 gc, linux-amd64)
# Problematic frame:
# C  [libucis.so+0x238b74]

......

For more details please refer to the attached log file "hs_err_pid21143.log".

Two approaches were tried to solve this problem but neither has worked.
Method.1:

Setting larger heap size of Java process by "-memlimit" options.For example "vmanager -memlimit 8G".

Method.2:

Enlarging stack memory size limit of the Coverage engine by setting "IMC_NATIVE_STACKSIZE" environment variable to a larger value. For example "setenv IMC_NATIVE_STACKSIZE 1024000"

According to "hs_err_pid*.log" it is almost certain that the memory overflow triggered Java's CrashOnOutOfMemoryError and caused Verisium Manager to crash. There are some arguments about memory management of Java like "Xms, Xmx, ThreadStackSize, Xss5048k etc" and maybe this problem can be fixed by setting these arguments during analysis. However, how exactly does Verisium Manager specify these arguments during analysis? I tried to set them by the form of setting environment variables before analysis but it didn't work in analysis and their values didn't change.

Is there something wrong with my operation or is there a better solution?

Thank you very much.

Hi, 

I am running a regression in order to collect the coverage. However I have an issue. I am setting a signal to 0 when reset is de-asserted  then this signal takes a fixed value when the reset is asserted. 

if(!rst_n) 
init_val= 'b0;

else 

init_val= 31'h34013FF7

the issue is that I got 0%  coverage for the init_value since we only have a rising edge and the signal is not toggling during the simulation. is there an option to collect coverage when there is a rising edge or a falling edge? 

I have been using the rank feature to identify tests that are redundant in our environment, but then I realized I'd also like to be able to see exactly what coverage goes into increasing the delta_cov value for a given test. If I had a test in my rank report that contributed 0.5% of the delta_cov, how could I got about seeing exactly where that 0.5% was coming from? It seems like that might be part of the correlate function, but I couldn't mange to find a way to see what specific coverage was being contributed for a given test.

Hi

I have fixed a SPACING RULE (SP1) for a CLASS_DIFF_PAIR whereas for via associated to the net (DP_VIA), the DISTANCE > 60mils respect to ANY other vias (PTH, BB, TEST vias)

Now my problem is that this rules should NOT be applied for GND VIAS (STICHING VIA) which must be placed at a distance < 40mils respect to DP_VIA

How to create an exemption to the SPACING RULE (SP1)?

Power efficiency is a critical factor in the fast-evolving world of semiconductor design.

The IEEE 1801 standard, also known as UPF (Unified Power Format), was developed by the IEEE to address the intricate challenges associated with power management in contemporary semiconductor designs. This standard offers a uniform framework for defining power domains, power states, and power intent, ensuring consistency across diverse tools and phases of the design process. By utilizing UPF, you can precisely model and regulate power consumption, a critical aspect for battery-operated devices, high-performance computing, and energy-efficient designs.

The key concepts of IEEE 1801 are:

1. Power domains
2. Power states
3. Power gating and isolation
4. Power switches
5. Level shifters, isolation, and retention cells
6. Macro model

Based on these building blocks, you write the power intent of the design.

The power intent for the design includes identifying/implementing low-power strategies that provide a clear description of the power architecture of a design.

The power definitions can effectively manage power consumption and ensure the chip meets its power and performance requirements.

You can start by creating the Power Supply Network, which defines how power is supplied to the design's various power domains and logic cells.

What's the next step to build the file? How do you understand the various concepts related to IEEE 1801? How do you complete the rest of the power intent file?

Relax!

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This course is a complete tutorial for understanding the fundamentals of IEEE 1801 low-power specification format concepts. You learn about IEEE 1801 power supply networks, ground ports and nets, creating and connecting supply ports/nets, power domain, power switch, power states, defining isolation and level shifter strategies, hierarchical IEEE 1801, and various versions of the IEEE 1801. You also explore how power intent information can be used for a design across various flow stages, such as functional verification, synthesis, logic equivalency checking, place-and-route, test, timing signoff, power integrity, and so forth, using Cadence^® tools.

Labs

We ensure that your learning journey is smooth with hands-on labs covering various design scenarios.

Lab Videos

Now, the exciting part is that to help you further, we have created engaging videos of the training labs. You can refer to the lab module's instructions in demo format at https://support.cadence.com.

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Lab Demo: Checking Power Intent for The Macro Connections in IEEE 1801 Format And Running IEEE 1801 Quality Checks using Conformal Low Power 

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Extract

Living with a respiratory illness requires patients to manage a wide range of symptoms, many of which will worsen as a disease progresses. Breathlessness is a hallmark feature of respiratory conditions, occurring in almost all individuals with COPD and interstitial lung disease (ILD) [1, 2]. Cough is present in 78% of people with ILD and is frequently distressing, with physical, social and emotional impacts [1, 3].

Bronchiectasis is a chronic lung condition which is characterised by recurrent chest infections, chronic sputum production and cough, and limited exercise tolerance. While bronchiectasis may be caused by various aetiologies, these features are shared by most patients with bronchiectasis regardless of the cause. This review consolidates the existing evidence on patient-managed interventions for adults with bronchiectasis, while also outlining areas for future research. Airway clearance techniques and hyperosmolar agents are key components of the bronchiectasis management and consistently recommended for clinical implementation. Questions around their prescription, such as optimal sequence of delivery, are still to be answered. Pulmonary rehabilitation and exercise are also recommended for patients with bronchiectasis. Relatively strong evidence underpins this recommendation during a clinically stable stage of the disease, although the role of pulmonary rehabilitation following an exacerbation is still unclear. Additionally, self-management programmes feature prominently in bronchiectasis treatment, yet the lack of consensus regarding their definition and outcomes presents hurdles to establishing a cohesive evidence base. Moreover, cough, a cardinal symptom of bronchiectasis, warrants closer examination. Although managing cough in bronchiectasis may initially appear risky, further research is necessary to ascertain whether strategies employed in other respiratory conditions can be safely and effectively adapted to bronchiectasis, particularly through identifying patient responder populations and criteria where cough may not enhance airway clearance efficacy and its control is needed. Overall, there is a growing recognition of the importance of patient-managed interventions in the bronchiectasis management. Efforts to improve research methodologies and increase research funding are needed to further advance our understanding of these interventions, and their role in optimising patient care and outcomes.

Family physicians are fielding questions about cannabis --particularly for the use of cannabis for treatment of pain. Like about every substance ingested to treat medical conditions, cannabis has risks and benefits. But regarding evidence-based practice and practice-based recommendations for patients about cannabis use, the cart is in front of the horse. Cannabis use is still illegal at a federal level and a Schedule 1 drug, but most states have challenged federal law by decriminalizing or legalizing cannabis for a variety of uses. Research is difficult due to this federal status as a Schedule 1 drug since federal funding is not readily available to support research. As a result, physicians have little to no guidance about the clinical usefulness of the product. This article explores what we know and what we are learning about cannabis, and the authors provide clinical guidance for patient care based on this evidence.

Single maintenance and reliever therapy (SMART) is an asthma treatment approach that utilizes combined inhaled corticosteroids and long-acting β-agonists for maintenance and quick relief therapy. Despite the evidence for its benefits in asthma treatment and its adoption into American and international asthma guidelines and recommendations, SMART remains a practice of some debate. This article reviews the available evidence for SMART and offers guidance for its integration into comprehensive asthma management. Overall, short-acting β-agonist-only asthma therapy regimens should be avoided, regardless of condition severity (SOR A Recommendation). Family medicine clinicians should start SMART for patients requiring either GINA Step 3 or 4 therapy, especially if they have signs of poor adherence (SOR B Recommendation). Finally, use budesonide-formoterol over other inhaled corticosteroid/long-acting β-agonist combinations when implementing SMART (SOR B Recommendation).

Background:

Discharge communication between hospitalists and primary care clinicians is essential to improve care coordination, minimize adverse events, and decrease unplanned health services use. Health-related social needs are key drivers of health, and hospitalists and primary care clinicians value communicating social needs at discharge.

The pursuit of harnessing data for knowledge creation has been an enduring quest, with the advent of machine learning (ML) and artificial intelligence (AI) marking significant milestones in this journey. ML, a subset of AI, emerged as the practice of employing mathematical models to enable computers to learn and improve autonomously based on their experiences. In the pharmaceutical and biopharmaceutical sectors, a significant portion of manufacturing data remains untapped or insufficient for practical use. Recognizing the potential advantages of leveraging the available data for process design and optimization, manufacturers face the daunting challenge of data utilization. Diverse proprietary data formats and parallel data generation systems compound the complexity. The transition to Pharma 4.0 necessitates a paradigm shift in data capture, storage, and accessibility for manufacturing and process operations. This paper highlights the pivotal role of AI in converting process data into actionable knowledge to support critical functions throughout the whole product life cycle. Furthermore, it underscores the importance of maintaining compliance with data integrity guidelines, as mandated by regulatory bodies globally. Embracing AI-driven transformations is a crucial step toward shaping the future of the pharmaceutical industry, ensuring its competitiveness and resilience in an evolving landscape.

Extract

As of 15 May 2024, >775 million confirmed cases of COVID-19 and >7 million deaths have been reported to the World Health Organization [1]. Although most patients with COVID-19 survive, survivors are at risk of long COVID, the sequelae of the viral infection affecting multiple organ systems [2]. Long COVID poses a substantial burden to individuals and society, even with a conservative estimate of 10% prevalence among COVID-19 survivors [3–5]. However, as the symptoms of long COVID vary substantially, ranging from respiratory symptoms, such as dyspnoea and cough, to fatigue and cognitive impairment [6], developing a standard set of investigations and management protocols for patients with long COVID is challenging.

Background

Long COVID is a heterogeneous clinical syndrome characterised by a variety of reported symptoms and signs. Its clinical management is expected to differ significantly worldwide.

The management of chylothorax remains challenging given the limited evidence and significant heterogeneity in practice. In addition, there are no practical guidelines on the optimal approach to manage this complex condition. We convened an international group of 27 experts from 20 institutions across five countries and four specialties (pulmonary, interventional radiology, thoracic surgery and nutrition) with experience and expertise in managing adult patients with chylothorax. We performed a literature and internet search for reports addressing seven clinically relevant PICO (Patient, Intervention, Comparison and Outcome) questions pertaining to the management of adult patients with chylothorax. This consensus statement, consisting of best practice statements based on expert consensus addressing these seven PICO questions, was formulated by a systematic and rigorous process involving the evaluation of published evidence, augmented with provider experience. Panel members participated in the development of the final best practice statements using the modified Delphi technique. Our consensus statement aims to offer guidance in clinical decision making when managing patients with chylothorax while also identifying gaps in knowledge and informing future research.

Background

Halm's clinical stability criteria have long guided antibiotic treatment and hospital discharge decisions for patients hospitalised with community-acquired pneumonia (CAP). Originally introduced in 1998, these criteria were established based on a relatively small and select patient population. Consequently, our study aims to reassess their applicability in the management of CAP in a contemporary real-world setting.

The clinical impact of 16α-¹⁸F-fluoro-17β-estradiol (¹⁸F-FES) PET/CT on patient management has not been well investigated. The aim of this study was to assess the clinical impact of ¹⁸F-FES PET/CT on the management of patients with recurrent or metastatic breast cancer. Methods: Study subjects were identified retrospectively from a database of a prospective trial for postmarketing surveillance of ¹⁸F-FES between 2021 and 2023. Patients who were suspected or known to have recurrent or metastatic estrogen receptor–positive breast cancer based on a routine standard workup were included. Planned management before and actual management after ¹⁸F-FES PET/CT were assessed by 2 experienced medical oncologists via medical chart review. A 5-point questionnaire was provided to evaluate the value of ¹⁸F-FES PET/CT for management planning. The rate of intention-to-treat and interdisciplinary changes, and the impact of ¹⁸F-FES PET/CT according to PET/CT result or clinical indication, were examined. Results: Of the 344 included patients, 120 (35%) experienced a change in management after ¹⁸F-FES PET/CT. In 139 (40%) patients,¹⁸F-FES PET/CT supported the existing management decision without a change in management. Intention-to-treat and interdisciplinary changes accounted for 64% (77/120) and 68% (82/120) of all changes, respectively. A higher rate of change was observed when lesions were ¹⁸F-FES–negative (44% [36/81]) than ¹⁸F-FES–positive (30% [51/172]) or mixed ¹⁸F-FES–positive/negative (36% [33/91]). Regarding clinical indications, the highest rate of change was shown when evaluating the origins of metastasis of double primary cancers (64% [9/14]). Conclusion: ¹⁸F-FES PET/CT modified the management of recurrent or metastatic breast cancer, serving as an impactful imaging modality in clinical practice.

BackgroundOrlistat is recommended as an adjunct to diet and exercise for weight loss in the treatment of type 2 diabetes mellitus (T2DM).AimTo explore associations between patient characteristics and orlistat prescribing, and to determine associations of orlistat with weight loss in T2DM and prediabetes.Design and settingCohort study using anonymised health records from a UK database of general practice.MethodThe UK Clinical Practice Research Datalink (CPRD) Aurum database was searched to compile a cohort of patients aged ≥18 years, first diagnosed with T2DM or prediabetes in 2016 or 2017. Once the data had been collated, multivariable logistic regression models were used to determine associations with starting orlistat and stopping it early (<12 weeks of prescriptions) and orlistat’s associations with weight loss in those who had not been prescribed second-line antidiabetic medications.ResultsOut of 100 552 patients with incident T2DM or prediabetes, 655 (0.8%) patients with T2DM and 128 (0.7%) patients with prediabetes were prescribed orlistat. Younger people, females, those in areas of deprivation, current smokers, those coprescribed metformin, and those recorded as having hypertension were statistically significantly more likely to be prescribed orlistat; higher baseline glycated haemoglobin levels were associated with early stopping. In comparison with patients not on orlistat, those who continued using it for ≥12 weeks were more likely to lose ≥5% weight (adjusted odds ratio [AOR] 1.69, 95% confidence interval [CI] = 1.07 to 2.67) but those who stopped orlistat early were less likely to lose ≥5% weight (AOR 0.56, 95% CI = 0.29 to 1.09).ConclusionOrlistat was significantly associated with weight loss in patients with T2DM and prediabetes when taken for at least 12 weeks; however, it was infrequently prescribed and often taken for <12 weeks. Orlistat may be a useful adjunct to lifestyle modifications for patients with T2DM and prediabetes, but barriers to continued use means it may not be effective for everyone in managing weight loss.