A systematic mapping of the advancing use of machine learning techniques for predictive maintenance in the manufacturing sector

The increasing availability of data, gathered by sensors and intelligent machines, is chang-ing the way decisions are made in the manufacturing sector. In particular, based on predictive approach and facilitated by the nowadays growing capabilities of hardware, cloud-based solutions, and new learning approaches, maintenance can be scheduled—over cell engagement and resource monitoring—when required, for minimizing (or managing) unexpected equipment failures, improving uptime through less aggressive maintenance schedules, shortening unplanned downtime, reducing excess (direct and indirect) cost, reducing long-term damage to machines and processes, and improve safety plans. With access to increased levels of data (and over learning mechanisms), companies have the capability to conduct statistical tests using machine learning algorithms, in order to uncover root causes of problems previously unknown. This study analyses the maturity level and contributions of machine learning methods for predictive maintenance. An upward trend in publications for predictive maintenance using machine learning techniques was identified with the USA and China leading. A mapping study—steady set until early 2019 data—was employed as a formal and well-structured method to synthesize material and to report on pervasive areas of research. Type of equipment, sensors, and data are mapped to properly assist new researchers in positioning new research activities in the domain of smart maintenance. Hence, in this paper, we focus on data-driven methods for predictive maintenance (PdM) with a comprehensive survey on applications and methods until, for the sake of commenting on stable proposal, 2019 (early included). An equal repartition between evaluation and validation studies was identified, this being a symptom of an immature but growing research area. In addition, the type of contribution is mainly in the form of models and methodologies. Vibrational signal was marked as the most used data set for diagnosis in manufacturing machinery monitoring; furthermore, supervised learning is reported as the most used predictive approach (ensemble learning is growing fast). Neural networks, followed by random forests and support vector machines, were identified as the most applied methods encompassing 40% of publications, of which 67% related to deep neural network with long short-term memory predominance. Notwithstanding, there is no robust approach (no one reported optimal performance over different case tests) that works best for every problem. We finally conclude the research in this area is moving fast to gather a separate focused analysis over the last two years (whenever stable implementations will appear)

Kidney transplantation from living donor with monolateral renal artery fibromuscular dysplasia using a cryopreserved iliac graft for arterial reconstruction: a case report and review of the literature

Background Aging and mortality of patients on waiting lists for kidney transplantation have increased, as a result of the shortage of organs available all over the world. Living donor grafts represent a significant source to maintain the donor pool, and resorting successfully to allografts with arterial disease has become a necessity. The incidence of renal artery fibromuscular dysplasia (FMD) in potential living renal donors is reported to be 2-6%, and up to 4% of them present concurrent extra-renal involvement. Case presentation We present a case of renal transplantation using a kidney from a living donor with monolateral FMD. Resection of the affected arterial segment and its subsequent replacement with a cryopreserved iliac artery graft from a deceased donor were performed. No intraoperative nor post-operative complications were reported. The allograft function promptly resumed, with satisfying creatinine clearance, and adequate patency of the vascular anastomoses was detected by Doppler ultrasounds. Conclusion Literature lacks clear guidelines on the eligibility of potential living renal donors with asymptomatic FMD. Preliminary assessment of the FMD living donor should always rule out any extra-renal involvement. Whenever possible, resection and reconstruction of the affected arterial segment should be taken into consideration as this condition may progress after implantation

Incidence and clinical predictors of a subsequent nonmelanoma skin cancer in solid organ transplant recipients with a first nonmelanoma skin cancer: a multicenter cohort study.

Objective: To compare the long-term risk of primary nonmelanoma skin cancer (NMSC) and the risk of subsequent NMSC in kidney and heart transplant recipients. Design: Partially retrospective cohort study. Setting: Two Italian transplantation centers. Patients: The study included 1934 patients: 1476 renal transplant recipients and 458 heart transplant recipients. Main Outcome Measures: Cumulative incidences and risk factors of the first and subsequent NMSCs. Results: Two hundred patients developed a first NMSC after a median follow-up of 6.8 years after transplantation. The 3-year risk of the primary NMSC was 2.1%. Of the 200 patients with a primary NMSC, 91 (45.5%) had a secondNMSCafter a median follow-up after the firstNMSC of 1.4 years (range, 3 months to 10 years). The 3-year risk of a second NMSC was 32.2%, and it was 49 times higher than that in patients with no previous NMSC. In a Cox proportional hazards regression model, age older than 50 years at the time of transplantation and male sex were significantly related to the first NMSC. Occurrence of the subsequent NMSC was not related to any risk factor considered, including sex, age at transplantation, type of transplanted organ, type of immunosuppressive therapy, histologic type of the first NMSC, and time since diagnosis of the first NMSC. Histologic type of the first NMSC strongly predicted the type of the subsequent NMSC. Conclusions: Development of a first NMSC confers a high risk of a subsequent NMSC in transplant recipients. Intensive long-term dermatologic follow-up of these patients is advisable

A Systematic Review of the Efficacy and Toxicity of Brachytherapy Boost Combined with External Beam Radiotherapy for Nonmetastatic Prostate Cancer

Context The optimum use of brachytherapy (BT) combined with external beam radiotherapy (EBRT) for localised/locally advanced prostate cancer (PCa) remains uncertain. Objective To perform a systematic review to determine the benefits and harms of EBRT-BT. Evidence acquisition Ovid MEDLINE, Embase, and EBM Reviews—Cochrane Central Register of Controlled Trials databases were systematically searched for studies published between January 1, 2000 and June 7, 2022, according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. Eligible studies compared low- or high-dose-rate EBRT-BT against EBRT ± androgen deprivation therapy (ADT) and/or radical prostatectomy (RP) ± postoperative radiotherapy (RP ± EBRT). The main outcomes were biochemical progression-free survival (bPFS), severe late genitourinary (GU)/gastrointestinal toxicity, metastasis-free survival (MFS), cancer-specific survival (CSS), and overall survival (OS), at/beyond 5 yr. Risk of bias was assessed and confounding assessment was performed. A meta-analysis was performed for randomised controlled trials (RCTs). Evidence synthesis Seventy-three studies were included (two RCTs, seven prospective studies, and 64 retrospective studies). Most studies included participants with intermediate-or high-risk PCa. Most studies, including both RCTs, used ADT with EBRT-BT. Generally, EBRT-BT was associated with improved bPFS compared with EBRT, but similar MFS, CSS, and OS. A meta-analysis of the two RCTs showed superior bPFS with EBRT-BT (estimated fixed-effect hazard ratio [HR] 0.54 [95% confidence interval {CI} 0.40–0.72], p < 0.001), with absolute improvements in bPFS at 5–6 yr of 4.9–16%. However, no difference was seen for MFS (HR 0.84 [95% CI 0.53–1.28], p = 0.4) or OS (HR 0.87 [95% CI 0.63–1.19], p = 0.4). Fewer studies examined RP ± EBRT. There is an increased risk of severe late GU toxicity, especially with low-dose-rate EBRT-BT, with some evidence of increased prevalence of severe GU toxicity at 5–6 yr of 6.4–7% across the two RCTs. Conclusions EBRT-BT can be considered for unfavourable intermediate/high-risk localised/locally advanced PCa in patients with good urinary function, although the strength of this recommendation based on the European Association of Urology guideline methodology is weak given that it is based on improvements in biochemical control. Patient summary We found good evidence that radiotherapy combined with brachytherapy keeps prostate cancer controlled for longer, but it could lead to worse urinary side effects than radiotherapy without brachytherapy, and its impact on cancer spread and patient survival is less clear

Monitoring of cellular immunity by interferon gamma (IFN-g) Enzyme-Linked Immunosorbent Spot (ELISPOT) assay in kidney allograft recipients: preliminary results of a longitudinal study.

Several efforts have been made in past years to identify markers for patients at heightened risk of acute and chronic immune-mediated allograft rejection. The ex vivo monitoring of cellular immunity by Enzyme-Linked Immunosorbent Spot (ELISPOT) assay has recently emerged as a primary tool in predicting either short and long-term outcomes in kidney allograft recipients. Therefore we started the systematic application of Interferon-gamma (IFN-g) ELISPOT assay to measure the frequency of producing IFN-g in recipient peripheral blood lymphocytes (PBLs) stimulated with donor lymphocytes before and 7, 14, 21,28 and 60 days after the transplant, respectively. Very preliminary results on 8 kidney transplant patients indicated that the number of HLA mismatches never correlated with the number of IFN-g spots. The frequencies of pre-transplant IFN-g spots were positively and significantly correlated with the number of post-transplant IFN-\ue3 spots. Clinical outcome was better in recipients with low frequencies than in recipients with high frequencies of pre and/or post-transplant IFN-g spots. The highest pre-and post-transplant number of IFN-g spots was observed in a patient who developed early acute rejection. Significant increases of the number of IFN-g spots preceded the onset of acute rejection events and decreased after supplemental i.v. steroid administration. Considering the low number of observations, these preliminary results must be considered cautiously; nevertheless we are encouraged to extend the systematic application of serial IFN-g ELISPOT assay measurements in a more consistent cohort of patients

Specific alloantigen self-control by regulatory T cells in organ transplantation: a review.

Multidrug immunosuppressive protocols have increased short-term patient and graft survival rates from 50% to 90% in the past two decades. Unfortunately, chronic graft rejection still remains the main cause of long-term failure and patients must undergo lifelong immunosuppression. The severe side effects such as life-threatening infections, secondary malignancies, and cardiovascular dysfunction all together include roughly 50% of deaths among kidney transplant patients with functioning grafts. Therefore, it should be of crucial importance to reduce immunosuppression and seek induction of specific tolerance to donor alloantigens. Several investigations have suggested that the acquisition of tolerance to self and/or foreign antigens is dependent on the number and function of naturally occurring and acquired regulatory T cells, which can control all aggressive T cells. The regulatory T cells together with their receptors, costimulatory molecules, cytokines, chemokines, and growth factors all contribute to maintain an equilibrium between aggressive and suppressive effector immune responses. As a consequence of increased knowledge, new immunosuppressive approaches based on either alloantigen-specific regulatory T-cell expansion in vivo or in vitro have been proposed to achieve donor-specific transplantation tolerance in kidney allograft recipients. This contribution attempted to summarize knowledge about regulatory T cells and developing methods to induce specific tolerance in kidney transplantation

Cloud services categories identification from requirements specifications

In the Cloud Computing field, with the increasing number of Cloud Services available thanks to several cloud providers, looking for a particular service has become very difficult, especially with the evolution of the stakeholders' needs. At the same time requirements specifications have become more and more complex to define in a formal representation and to analyse, since the stakeholders' goals are typically high-level, abstract, and hard-to-measure. For these reasons it would be useful to automate, as much as possible, requirements analysis. In this work we propose an automatic classification and modelling of requirements that are expressed in a natural language form, and an automatic identification of cloud services categories from requirements in order to support the development of a cloud application. Automated requirements analysis is not an easy subject, due to the natural languages variability and ambiguity, that's why different machine/deep learning and natural language processing approaches are used and compared. The target data set is provided by the Open-Security tera-PROMISE repository

A fuzzy prolog and ontology driven framework for medical diagnosis using IoT devices

Advances in medical care and computer technology in recent decades have expanded the parameters of the traditional domain of medical services. This scenario has created new opportunities for building applications to provide enterprise services in an efficient, diverse and highly dynamic environment. Moreover the IoT revolution is redesigning modern health care with promising technological prospect and has made IT-based healthcare systems expensive, competitive and complex. Their complexity is also enhanced by the use of semantic models which allow the detection and prediction of a patient health anomalies and the therapy management is produced accordingly. In this paper will be presented a prototypical framework that, starting from the stream analysis and processing coming from wearable devices, tries to detect the possible health anomalies in real time and, through a heuristic and ontology-driven approach capable of reasoning on the patient’s conditions, it gives hints about possible diseases that are currently going on