ASTEF: A Simple Tool for Examining Fixation

In human factors and ergonomics research, the analysis of eye movements has gained popularity as a method for obtaining information concerning the operator's cognitive strategies and for drawing inferences about the cognitive state of an individual. For example, recent studies have shown that the distribution of eye fixations is sensitive to variations in mental workload---dispersed when workload is high, and clustered when workload is low. Spatial statistics algorithms can be used to obtain information about the type of distribution and can be applied over fixations recorded during small epochs of time to assess online changes in the level of mental load experienced by the individuals. In order to ease the computation of the statistical index and to encourage research on the spatial properties of visual scanning, A Simple Tool for Examining Fixations has been developed. The software application implements functions for fixation visualization, management, and analysis, and includes a tool for fixation identification from raw gaze point data. Updated information can be obtained online at www.astef.info, where the installation package is freely downloadable

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)

Utility of preoperative neutrophil/lymphocyte ratio as a new objective prognostic tool in endoscopically treated upper tract urothelial carcinoma. A retrospective evaluation

Introduction: This exploratory retrospective analysis examined any potential prognostic role of preoperative neutrophil lymphocyte ratio (NLR) for progression-free survival (PFS) and time to endoscopically verified upper tract or bladder recurrence-free survival (RFS) in upper tract urothelial cancer (UTUC) patients selected for endoscopic treatment with subsequent endosurveillance. Patients and Methods: Eligibility criteria were natural orifice endoscopically retrogradely treated low-risk and imperative UTUC patients treated between 2005 and 2019, with biopsy confirmed diagnosis and 12 months minimum follow-up. For PFS, optimal NLR cutoff value was derived by log-rank test. Subsequently, both PFS and RFS were assessed for differences using Kaplan-Meier survival curves and log-rank test. Multivariate proportional Cox regression analysis adjusted for clinicopathologic variables was performed to examine end points for NLR-independent prognostic significance. Results: There were 100 eligible patients (63 truly low risk and 37 imperative cases). The optimal PFS log-rank test NLR cutoff value was 2.7. NLR ≥2.7 was significantly associated with shorter PFS (p = 0.01), and shorter upper tract RFS (p = 0.03), but not with bladder RFS (p = 0.90). Only positive high-grade cytology (hazard ratio [HR] 5.92, 95% confidence interval [CI] 2.140-16.35, p = 0.002) and NLR ≥2.7 (HR 4.28, 95% CI 1.34-13.72, p = 0.014) independently predicted PFS in multivariate analysis. Recurrence and progression were not significantly linked in the low-risk subset. Conclusions: This exploratory analysis showed that baseline NLR evaluation before first endoscopic UTUC treatment may be a valuable predictor and prognosticator of defined disease progression and of upper tract recurrence risk. In conjunction with high-grade urine cytology, NLR may improve risk stratification to optimize future individualized management

Percezione del rischio da frana: uno studio preliminare nel comune di Sarno (SA)

La nota presenta i risultati preliminari di un’indagine sulla percezione del rischio da frana da parte degli abitanti del comune di Sarno e sulla loro opinione e grado di conoscenza riguardo gli interventi di mitigazione del rischio, di tipo strutturale e non strutturale, realizzati a Sarno a seguito dei disastrosi eventi franosi del 5-6 maggio 1998. L’indagine è stata condotta, attraverso l’utilizzo di un questionario appositamente sviluppato allo scopo, nei mesi di marzo, aprile e maggio 2013. Il questionario è stato somministrato, attraverso interviste individuali, a 100 abitanti del Comune di Sarno residenti nella frazione di Episcopio, ovvero all’interno dell’area che ha registrato il maggior numero di vittime a seguito degli eventi del maggio 1998

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

Community-based risk management strategies: a survey on landslide risk knowledge and perception

People know and perceive risk differently. This study investigates perceptions, knowledge and opinions on landslide risk by the residents of Sarno, a small town in southern Italy, which was significantly affected by disastrous landslides on 5-6 May 1998. The paper presents the main results of a survey conducted in the months of March, April and May 2013, using a purposefully developed questionnaire. The questionnaire was administered through individual interviews to 100 residents, chosen to include a significant percentage of people living inside (60) and outside (40) the so-called “red zone”, a territory declared at high residual risk after the events of 1998

Community-based risk management strategies: a survey on landslide risk knowledge and perception

People know and perceive risk differently. This study investigates perceptions, knowledge and opinions on landslide risk by the residents of Sarno, a small town in southern Italy, which was significantly affected by disastrous landslides on 5-6 May 1998. The paper presents the main results of a survey conducted in the months of March, April and May 2013, using a purposefully developed questionnaire. The questionnaire was administered through individual interviews to 100 residents, chosen to include a significant percentage of people living inside (60) and outside (40) the so-called “red zone”, a territory declared at high residual risk after the events of 1998

Automatic Classification of Road Traffic with Fiber Based Sensors in Smart Cities Applications

Low cost monitoring of road traffic can bring a significant contribution to use the smart cities perspective for safety. The possibility of sensing and classifying vehicles and march conditions by means of simple physical sensors may support both real time applications and studies on traffic dynamics, e.g. support and assistance for car crashes and prevention of accidents, and maintenance planning or support to trials in case of litigation. Optical fibers technology is well known for its wide adoption in data transmissions as a commodity component of computer networks: its popularity led to large availability on the market of high quality fiber at affordable price. As a purely physical application, its optical properties may be exploited to monitor in real time mechanical solicitations the fiber undergoes. In this paper we present a novel approach to using optical fibers as road sensors. As quite popular in literature, fiber is used to sense the vibrations caused by vehicles on the road: in our case, signals are processed by functional classification techniques to obtain a higher quality and a larger flexibility for the reuse of results. Classification aims at enabling profiling of road traffic. Moreover in our approach we would like to optimise the analysis and classification computations by splitting the process among edge nodes and cloud nodes according to the available computation capacity. Our solution has been tested by an experimental campaign to show the suitability of the approach

Performance evaluation for the design of a hybrid cloud based distance synchronous and asynchronous learning architecture

The COVID-19 emergency suddenly obliged schools and universities around the world to deliver on-line lectures and services. While the urgency of response resulted in a fast and massive adoption of standard, public on-line platforms, generally owned by big players in the digital services market, this does not sufficiently take into account privacy-related and security-related issues and potential legal problems about the legitimate exploitation of the intellectual rights about contents. However, the experience brought to attention a vast set of issues, which have been addressed by implementing these services by means of private platforms. This work presents a modeling and evaluation framework, defined on a set of high-level, management-oriented parameters and based on a Vectorial Auto Regressive Fractional (Integrated) Moving Average based approach, to support the design of distance learning architectures. The purpose of this framework is to help decision makers to evaluate the requirements and the costs of hybrid cloud technology solutions. Furthermore, it aims at providing a coarse grain reference organization integrating low-cost, long-term storage management services to implement a viable and accessible history feature for all materials. The proposed solution has been designed bearing in mind the ecosystem of Italian universities. A realistic case study has been shaped on the needs of an important, generalist, polycentric Italian university, where some of the authors of this paper work