Bounds on the Capacity of the Relay Channel with Noncausal State Information at Source

We consider a three-terminal state-dependent relay channel with the channel state available non-causally at only the source. Such a model may be of interest for node cooperation in the framework of cognition, i.e., collaborative signal transmission involving cognitive and non-cognitive radios. We study the capacity of this communication model. One principal problem in this setup is caused by the relay's not knowing the channel state. In the discrete memoryless (DM) case, we establish lower bounds on channel capacity. For the Gaussian case, we derive lower and upper bounds on the channel capacity. The upper bound is strictly better than the cut-set upper bound. We show that one of the developed lower bounds comes close to the upper bound, asymptotically, for certain ranges of rates.Comment: 5 pages, submitted to 2010 IEEE International Symposium on Information Theor

Facilities components’ reliability & maintenance services self-rating through big data processing

The availability of big data in the information modelling of buildings can be useful to improve maintenance strategies and activities that are integrated in a digital twin. In some countries, such as Italy, tender specifications for public works must avoid any reference to specific brands and models, both in building design and maintenance services: quality levels and service-life objectives must be defined solely through performance specifications with reference to national or international standards. This could be a critical issue when considering reliability and serviceability of facility components, because there are no official methods about the ratings or measurements on the aforementioned performances. To help solving this concern, a method is proposed to broaden the scope of the big data collected from IoT applied to facility components, so as to feed a general and public database capable of normalizing data on faults and the effects of maintenance interventions, e.g. by correlating them with actual running times and operating conditions. In this way, each component on the market can theoretically feed a public and accessible database that collects reports on the occurrence of faults and the maintenance results, thus statistically processing its propensity for durability, the effectiveness of maintenance, the maintainability propensity of components as well as their reliability (e.g. by assessing the interval between maintenance interventions). In this way, a standardization of reliability, maintainability and durability performances ratings for components and serviceability performance rating for facility maintenance services could boost the facility quality design and improve the maintenance management

Machine Learning Framework for the Sustainable Maintenance of Building Facilities

The importance of sustainable building maintenance is growing as part of the Sustainable Building concept. The integration and implementation of new technologies such as the Internet of Things (IoT), smart sensors, and information and communication technology (ICT) into building facilities generate a large amount of data that will be utilized to better manage the sustainable building maintenance and staff. Anomaly prediction models assist facility managers in informing operators to perform scheduled maintenance and visualizing predicted facility anomalies on building information models (BIM). This study proposes a Machine Learning (ML) anomaly prediction model for sustainable building facility maintenance using an IoT sensor network and a BIM model. The suggested framework shows the data management technique of the anomaly prediction model in the 3D building model. The case study demonstrated the framework’s competence to predict anomalies in the heating ventilation air conditioning (HVAC) system. Furthermore, data collected from various simulated conditions of the building facilities was utilized to monitor and forecast anomalies in the 3D model of the fan coil. The faults were then predicted using a classification model, and the results of the models are introduced. Finally, the IoT data from the building facility and the predicted values of the ML models are visualized in the building facility’s BIM model and the real-time monitoring dashboard, respectively

IRIS: methodological assessment of psychopathological disease in a cohort of hirsute women.

Hirsutism in females can be a source of considerable psychological distress and a threat to female identity. The aim of our study was to evaluate a possible relationship between facial, total body hair involvement and physical, mental and social well--being during 12 months of follow--up and treatment. Both objective and subjective methods of evaluating hirsutism were used: the Ferriman-Gallwey scoring method and the questionnaires GHQ--12, PCOSQ and SF--12. The total of 469 female patients (mean age 27.61±7.63 years) was enrolled in 27 Italian centres participating in this study. Higher total body score was correlated to significant emotional discomfort. The correlation between the FG total body score, the facial score and physical/mental health was found to be significant in all the patients assessed by SF--12 questionnaire. The ongoing reduction of GHQ--12 score was found for the facial FG score at the first follow--up (T0--T1 period) and at the second one (T0--T2). No relationship was found between T1 and T2. At both six (T1) and twelve months (T2) follow--up an increase of PCOSQ score (psychological improvement) was accompanied by a concomitant reduction of the FG score (reduction of hirsutism). Physical health assessed by SF--12 questionnaire does not change at both six and twelve months' follow--up, but mental health decreased at both T1 and T2. The clinical improvement was achieved at six months regardless on treatment used and it was maintained for the next six--month's follow--up. The clinical outcome could be assessed both by Ferriman--Gallwey score both through questionnaires administrated to each patient with hirsutism. For the evaluation of psychopathological discomfort the most appropriate questionnaire was GHQ--12, because of it major sensitivity to identify the psychological discomfort in the hirsutism

Analisi e modellizzazione dell'effetto di agrotecniche sull'altezza della pianta : il progetto MIATA

L\u2019altezza delle piante \ue8 importante per determinarne il potenziale produttivo e la suscettibilit\ue0 nei confronti di avversit\ue0 abio-tiche. Nonostante questo, i modelli disponibili la ignorano o la simulano utilizzando semplici funzioni logistiche indipendenti dai reali processi bioLsici in gioco e dalle modalit\ue0 di gestione. Il progetto MIATA, condotto da studenti del corso di Sistemi Colturali dell\u2019Universit\ue0 degli Studi di Milano, ha affrontato la problematica, fornendo soluzioni modellistiche utili sia a scopo previsionale che di supporto alla gestione

Comparing Long-Acting Antipsychotic Discontinuation Rates Under Ordinary Clinical Circumstances: A Survival Analysis from an Observational, Pragmatic Study

Background: Recent guidelines suggested a wider use of long-acting injectable antipsychotics (LAI) than previously, but naturalistic data on the consequences of LAI use in terms of discontinuation rates and associated factors are still sparse, making it hard for clinicians to be informed on plausible treatment courses. Objective: Our objective was to assess, under real-world clinical circumstances, LAI discontinuation rates over a period of 12 months after a first prescription, reasons for discontinuation, and associated factors. Methods: The STAR Network ‘Depot Study’ was a naturalistic, multicentre, observational prospective study that enrolled subjects initiating a LAI without restrictions on diagnosis, clinical severity or setting. Participants from 32 Italian centres were assessed at baseline and at 6 and 12 months of follow-up. Psychopathology, drug attitude and treatment adherence were measured using the Brief Psychiatric Rating Scale, the Drug Attitude Inventory and the Kemp scale, respectively. Results: The study followed 394 participants for 12 months. The overall discontinuation rate at 12 months was 39.3% (95% confidence interval [CI] 34.4–44.3), with paliperidone LAI being the least discontinued LAI (33.9%; 95% CI 25.3–43.5) and olanzapine LAI the most discontinued (62.5%; 95% CI 35.4–84.8). The most frequent reason for discontinuation was onset of adverse events (32.9%; 95% CI 25.6–40.9) followed by participant refusal of the medication (20.6%; 95% CI 14.6–27.9). Medication adherence at baseline was negatively associated with discontinuation risk (hazard ratio [HR] 0.853; 95% CI 0.742–0.981; p = 0.026), whereas being prescribed olanzapine LAI was associated with increased discontinuation risk compared with being prescribed paliperidone LAI (HR 2.156; 95% CI 1.003–4.634; p = 0.049). Conclusions: Clinicians should be aware that LAI discontinuation is a frequent occurrence. LAI choice should be carefully discussed with the patient, taking into account individual characteristics and possible obstacles related to the practicalities of each formulation

Combinations of QT-prolonging drugs: towards disentangling pharmacokinetic and pharmaco-dynamic effects in their potentially additive nature.

Background: Whether arrhythmia risks will increase if drugs with electrocardiographic (ECG) QT-prolonging properties are combined is generally supposed but not well studied. Based on available evidence, the Arizona Center for Education and Research on Therapeutics (AZCERT) classification defines the risk of QT prolongation for exposure to single drugs. We aimed to investigate how combining AZCERT drug categories impacts QT duration and how relative drug exposure affects the extent of pharmacodynamic drug–drug interactions. Methods: In a cohort of 2558 psychiatric inpatients and outpatients, we modeled whether AZCERT class and number of coprescribed QT-prolonging drugs correlates with observed rate-corrected QT duration (QTc) while also considering age, sex, inpatient status, and other QTc-prolonging risk factors. We concurrently considered administered drug doses and pharmacokinetic interactions modulating drug clearance to calculate individual weights of relative exposure with AZCERT drugs. Because QTc duration is concentration-dependent, we estimated individual drug exposure with these drugs and included this information as weights in weighted regression analyses. Results: Drugs attributing a ‘known’ risk for clinical consequences were associated with the largest QTc prolongations. However, the presence of at least two versus one QTc-prolonging drug yielded nonsignificant prolongations [exposure-weighted parameter estimates with 95% confidence intervals for ‘known’ risk drugs + 0.93 ms (–8.88;10.75)]. Estimates for the ‘conditional’ risk class increased upon refinement with relative drug exposure and coadministration of a ‘known’ risk drug as a further risk factor. Conclusions: These observations indicate that indiscriminate combinations of QTc-prolonging drugs do not necessarily result in additive QTc prolongation and suggest that QT prolongation caused by drug combinations strongly depends on the nature of the combination partners and individual drug exposure. Concurrently, it stresses the value of the AZCERT classification also for the risk prediction of combination therapies with QT-prolonging drugs

Off–label long acting injectable antipsychotics in real–world clinical practice: a cross-sectional analysis of prescriptive patterns from the STAR Network DEPOT study

Introduction: Information on the off–label use of Long–Acting Injectable (LAI) antipsychotics in the real world is lacking. In this study, we aimed to identify the sociodemographic and clinical features of patients treated with on– vs off–label LAIs and predictors of off–label First– or Second–Generation Antipsychotic (FGA vs. SGA) LAI choice in everyday clinical practice. Method: In a naturalistic national cohort of 449 patients who initiated LAI treatment in the STAR Network Depot Study, two groups were identified based on off– or on–label prescriptions. A multivariate logistic regression analysis was used to test several clinically relevant variables and identify those associated with the choice of FGA vs SGA prescription in the off–label group. Results: SGA LAIs were more commonly prescribed in everyday practice, without significant differences in their on– and off–label use. Approximately 1 in 4 patients received an off–label prescription. In the off–label group, the most frequent diagnoses were bipolar disorder (67.5%) or any personality disorder (23.7%). FGA vs SGA LAI choice was significantly associated with BPRS thought disorder (OR = 1.22, CI95% 1.04 to 1.43, p = 0.015) and hostility/suspiciousness (OR = 0.83, CI95% 0.71 to 0.97, p = 0.017) dimensions. The likelihood of receiving an SGA LAI grew steadily with the increase of the BPRS thought disturbance score. Conversely, a preference towards prescribing an FGA was observed with higher scores at the BPRS hostility/suspiciousness subscale. Conclusion: Our study is the first to identify predictors of FGA vs SGA choice in patients treated with off–label LAI antipsychotics. Demographic characteristics, i.e. age, sex, and substance/alcohol use co–morbidities did not appear to influence the choice towards FGAs or SGAs. Despite a lack of evidence, clinicians tend to favour FGA over SGA LAIs in bipolar or personality disorder patients with relevant hostility. Further research is needed to evaluate treatment adherence and clinical effectiveness of these prescriptive patterns