Detection of Deadlocks and Race Conditions in Computing Systems

A race condition is a phenomenon wherein the output of an electronic device or computer process (thread) depends on the relative timing of events outside the control of the process or device. A deadlock is a state in which multiple computing processes that share a common resource are stalled due to the processes mutually locking each other out of access to the resource. Deadlocks and race conditions are difficult bugs to detect, or even reproduce for debugging. This disclosure presents techniques that detect deadlocks and race conditions using machine-learning models to analyze the control flow graph of a program. Predictions of potential race or deadlock conditions are accompanied by justifications, e.g., potential scenarios that cause a race conditions or deadlock to arise. The classifying and generalizing abilities of machinelearning models are applied such that these difficult to detect bugs are caught at design stage, most advantageously for large code bases

Context-Aware Software Builds

Software libraries are of the static or dynamic variety. In a static library, code from the library is integrated into the executable at compile time. The resulting executable is relatively large but runs fast and in a stand-alone manner. In a dynamic library, code from the library is linked to the executable at run-time. The executable is smaller, and due to the sharing of dynamic libraries across processes, has less memory overhead. However, running the executable is contingent on the presence of the dynamic library in the machine that it runs on. Linking a library at run-time can also cause loss in speed. This disclosure presents machine-learning based techniques to optimally identify a build target as a shared or static library. A recommendation is made to the software developer regarding an optimal setting (dynamic or static) for compilation. The techniques enable a developer to make informed design decisions

Automatic Denormalization of Databases

Normalization is a technique in databases to eliminate data redundancy or inconsistent dependency. Normalizing is achieved by dividing larger tables of a database into smaller ones and defining relationships between them. Normalization can yield performance gains, such as improved response times, but only to an extent. Highly normalized databases are not performance optimal. Optimal database performance is often obtained at a sweet spot between optimizing sizes of individual tables and the number of tables. A highly normalized database is therefore often denormalized to improve performance. Traditionally, denormalization is driven by user or developer intuition. This disclosure describes a machine-learning model to optimally denormalize a database based on, e.g., typical database queries, frequency of queries, response-times, projections of response time upon denormalization, etc. The techniques result in an optimal normal form of the database, in turn resulting in superior data integrity and performance

Distributed Placement of Machine-Learning Computing in an Edge Network

Smart devices continue to proliferate as the Internet-of-Things expands. Collectively, Internet-of-Things devices generate massive amounts of data for processing, analysis, and implementation. However, most individual smart devices lack sufficient hardware resources to process collected data in an efficient or timely manner. Thus, most devices send their data to a remote server or other cloud-based computing system for processing because of the increased computational capacities of such remote locations. Although these remote locations can process the data faster and more efficiently, the increase in the number of smart devices accessing the remote locations increases the transmission traffic, and associated bottlenecks, on networks and other data-transmission systems. Many smart devices reside on local networks that feature other, more-powerful, computing devices, such as desktops, laptops, home servers, and gaming systems. Some of these additional computing devices could be tasked with processing data and other information for Internet-of-Things devices that lack sufficient computational capacity to process the data themselves

Transfer Inference Learning

Techniques are described that combine machine learning with an edge network that includes IoT devices to yield an effective and efficient method of assessing a condition of an environment. An inference module that includes a machine-learning algorithm, installed and executing on the IoT devices, assesses a condition detected from multiple, different geographic locations. The IoT devices transfer sets of data and inferences as well as respective sets of confidence levels to converge on a verified set of inferences. The verified set of inferences is arrived at quickly and with a high confidence level

Effectiveness of a national quality improvement programme to improve survival after emergency abdominal surgery (EPOCH): a stepped-wedge cluster-randomised trial

Background: Emergency abdominal surgery is associated with poor patient outcomes. We studied the effectiveness of a national quality improvement (QI) programme to implement a care pathway to improve survival for these patients. Methods: We did a stepped-wedge cluster-randomised trial of patients aged 40 years or older undergoing emergency open major abdominal surgery. Eligible UK National Health Service (NHS) hospitals (those that had an emergency general surgical service, a substantial volume of emergency abdominal surgery cases, and contributed data to the National Emergency Laparotomy Audit) were organised into 15 geographical clusters and commenced the QI programme in a random order, based on a computer-generated random sequence, over an 85-week period with one geographical cluster commencing the intervention every 5 weeks from the second to the 16th time period. Patients were masked to the study group, but it was not possible to mask hospital staff or investigators. The primary outcome measure was mortality within 90 days of surgery. Analyses were done on an intention-to-treat basis. This study is registered with the ISRCTN registry, number ISRCTN80682973. Findings: Treatment took place between March 3, 2014, and Oct 19, 2015. 22 754 patients were assessed for elegibility. Of 15 873 eligible patients from 93 NHS hospitals, primary outcome data were analysed for 8482 patients in the usual care group and 7374 in the QI group. Eight patients in the usual care group and nine patients in the QI group were not included in the analysis because of missing primary outcome data. The primary outcome of 90-day mortality occurred in 1210 (16%) patients in the QI group compared with 1393 (16%) patients in the usual care group (HR 1·11, 0·96–1·28). Interpretation: No survival benefit was observed from this QI programme to implement a care pathway for patients undergoing emergency abdominal surgery. Future QI programmes should ensure that teams have both the time and resources needed to improve patient care. Funding: National Institute for Health Research Health Services and Delivery Research Programme

Effectiveness of a national quality improvement programme to improve survival after emergency abdominal surgery (EPOCH): a stepped-wedge cluster-randomised trial

BACKGROUND: Emergency abdominal surgery is associated with poor patient outcomes. We studied the effectiveness of a national quality improvement (QI) programme to implement a care pathway to improve survival for these patients. METHODS: We did a stepped-wedge cluster-randomised trial of patients aged 40 years or older undergoing emergency open major abdominal surgery. Eligible UK National Health Service (NHS) hospitals (those that had an emergency general surgical service, a substantial volume of emergency abdominal surgery cases, and contributed data to the National Emergency Laparotomy Audit) were organised into 15 geographical clusters and commenced the QI programme in a random order, based on a computer-generated random sequence, over an 85-week period with one geographical cluster commencing the intervention every 5 weeks from the second to the 16th time period. Patients were masked to the study group, but it was not possible to mask hospital staff or investigators. The primary outcome measure was mortality within 90 days of surgery. Analyses were done on an intention-to-treat basis. This study is registered with the ISRCTN registry, number ISRCTN80682973. FINDINGS: Treatment took place between March 3, 2014, and Oct 19, 2015. 22 754 patients were assessed for elegibility. Of 15 873 eligible patients from 93 NHS hospitals, primary outcome data were analysed for 8482 patients in the usual care group and 7374 in the QI group. Eight patients in the usual care group and nine patients in the QI group were not included in the analysis because of missing primary outcome data. The primary outcome of 90-day mortality occurred in 1210 (16%) patients in the QI group compared with 1393 (16%) patients in the usual care group (HR 1·11, 0·96-1·28). INTERPRETATION: No survival benefit was observed from this QI programme to implement a care pathway for patients undergoing emergency abdominal surgery. Future QI programmes should ensure that teams have both the time and resources needed to improve patient care. FUNDING: National Institute for Health Research Health Services and Delivery Research Programme

Isolated supravalvular aortic stenosis with left ventricular diverticulum and cleft mitral valve: Surgical repair in adulthood

Supravalvular aortic stenosis is an uncommon but well characterized congenital narrowing of the ascending aorta above the level of the coronary arteries. It can be a familial disorder, can occur sporadically, or can be associated with Williams syndrome. We are reporting a very rare presentation of supravalvular aortic stenosis with associated left ventricular diverticulum and cleft mitral valve. Repair consisted of resection of the ascending aorta, patch augmentation of the aortic root, and mitral valve repair. Follow-up echocardiography demonstrated normal mitral and aortic valve function and a postoperative three-dimensional computed tomographic scan showed a normal shape of the reconstructed ascending aorta

The prevalence, characteristics, and impact of work-related musculoskeletal disorders among physical therapists in the Kingdom of Saudi Arabia – a cross-sectional study

BackgroundPhysical therapists are known to be susceptible to work-related musculoskeletal disorders (WMSDs), but the prevalence of WMSDs in Saudi Arabia has not been documented. This study aimed to establish the prevalence, characteristics, and risk factors of WMSDs among physical therapists in Saudi Arabia.Material and MethodsA cross-sectional study was conducted among 113 physical therapists in Saudi Arabia using a 6-component questionnaire. Descriptive statistics, incidence, percentages, and χ2 test were used for data analysis.ResultsThe response rate was 68.8%. The reported 12-month incidence of WMSDs was 83.8%. The low back (63.7%) was the most common site of these disorders, followed by the neck (59.2%), while the hip/thigh (4.4%) was the least involved body part. Incidence was related to gender: females were more affected than males (neck, shoulders, low back); age: younger therapists were more affected than older ones (shoulders, low back); working sector: government sector workers were more affected than those employed in other sectors (neck); and specialty: orthopedic specialists were the most frequently affected, followed by those specializing in neurology (thumbs, upper back, knees, ankle/foot). Most of the physical therapists had >5 periods of neck, shoulder, and low-back WMSDs. The most important risk factor for WMSDs was treating more patients in a day (47.7%). The most frequently adopted handling strategy identified to combat WMSDS was modifying the patient’s position (62.8%).ConclusionsOverall, WMSDs among physical therapists in Saudi Arabia are common, with the low back and the neck constituting the most frequently affected body regions. Professional experience and the awareness of ergonomics principles can help prevent the early development of WMSDs among physical therapists. Med Pr. 2021;72(4):363–7

Lipidomic Profiling Identifies a Novel Lipid Signature Associated with Ethnicity-Specific Disparity of Bladder Cancer

Bladder Cancer (BLCA) is the ninth most frequently diagnosed cancer globally and the sixth most common cancer in the US. African Americans (AA) exhibit half the BLCA incidence compared to European Americans (EA), but they have a 70% higher risk of cancer-related death; unfortunately, this disparity in BLCA mortality remains poorly understood. In this study, we have used an ethnicity-balanced cohort for unbiased lipidomics profiling to study the changes in the lipid fingerprint for AA and EA BLCA tissues collected from similar geographical regions to determine a signature of ethnic-specific alterations. We identified 86 lipids significantly altered between self-reported AA and EA BLCA patients from Augusta University (AU) cohort. The majority of altered lipids belong to phosphatidylcholines (PCs), phosphatidylethanolamines (PEs), ly sophosphatidylcholines (lysoPCs), phosphatidylserines (PSs), and diglycerides (DGs). Interestingly, levels of four lysoPCs (lyso PCs 20:3, lyso PCs 22:1, lyso PCs 22:2, and lyso PCs 26:1) were elevated while, in contrast, the majority of the PCs were reduced in AA BLCA. Significant alterations in long-chain monounsaturated (MonoUN) and polyunsaturated (PolyUN) lipids were also observed between AA and EA BLCA tumor tissues. These first-in-field results implicate ethnic-specific lipid alterations in BLCA