Optimal selection of contracts and work shifts in multi-skill call centers

This paper deals with the problem of finding the most suitable contracts to be used when hiring the operators of a call center and deciding their optimal working schedule, to optimize the trade-off between the service level provided to the customers and the cost of the personnel. In a previous paper (Cordone et al. 2011), we proposed a heuristic method to quickly build an integer solution from the solution of the continuous relaxation of an integer linear programming model. In this paper, we generalize that model to take into account a much wider class of working contracts, allowing heterogeneous shift patterns, as well as legal constraints related to continuously active working environments. Since our original rounding heuristic cannot be extended to the new model, due to its huge size and to the involved correlations between different sets of integer variables, we introduce a more sophisticated heuristic based on decomposition and on a multi-level iterative structure. We compare the results of this heuristic with those of a Greedy Randomized Adaptive Search Procedure, both on real-world instances and on realistic random instances

Intelligenza artificiale e sicurezza: opportunità, rischi e raccomandazioni

L'IA (o intelligenza artificiale) è una disciplina in forte espansione negli ultimi anni e lo sarà sempre più nel prossimo futuro: tuttavia è dal 1956 che l’IA studia l’emulazione dell’intelligenza da parte delle macchine, intese come software e in certi casi hardware. L’IA è nata dall’idea di costruire macchine che - ispirandosi ai processi legati all’intelligenza umana - siano in grado di risolvere problemi complessi, per i quali solitamente si ritiene che sia necessario un qualche tipo di ragionamento intelligente. La principale area di ricerca e applicazione attuale dell’IA è il machine learning (algoritmi che imparano e si adattano in base ai dati che ricevono), che negli ultimi anni ha trovato ampie applicazioni grazie alle reti neurali (modelli matematici composti da neuroni artificiali) che a loro volta hanno consentito la nascita del deep learning (reti neurali di maggiore complessità). Appartengono al mondo dell’IA anche i sistemi esperti, la visione artificiale, il riconoscimento vocale, l’elaborazione del linguaggio naturale, la robotica avanzata e alcune soluzioni di cybersecurity. Quando si parla di IA c'è chi ne è entusiasta pensando alle opportunità, altri sono preoccupati poiché temono tecnologie futuristiche di un mondo in cui i robot sostituiranno l'uomo, gli toglieranno il lavoro e decideranno al suo posto. In realtà l'IA è ampiamente utilizzata già oggi in molti campi, ad esempio nei cellulari, negli oggetti smart (IoT), nelle industry 4.0, per le smart city, nei sistemi di sicurezza informatica, nei sistemi di guida autonoma (drive o parking assistant), nei chat bot di vari siti web; questi sono solo alcuni esempi basati tutti su algoritmi tipici dell’intelligenza artificiale. Grazie all'IA le aziende possono avere svariati vantaggi nel fornire servizi avanzati, personalizzati, prevedere trend, anticipare le scelte degli utenti, ecc. Ma non è tutto oro quel che luccica: ci sono talvolta problemi tecnici, interrogativi etici, rischi di sicurezza, norme e legislazioni non del tutto chiare. Le organizzazioni che già adottano soluzioni basate sull’IA, o quelle che intendono farlo, potrebbero beneficiare di questa pubblicazione per approfondirne le opportunità, i rischi e le relative contromisure. La Community for Security del Clusit si augura che questa pubblicazione possa fornire ai lettori un utile quadro d’insieme di una realtà, come l’intelligenza artificiale, che ci accompagnerà sempre più nella vita personale, sociale e lavorativa.AI (or artificial intelligence) is a booming discipline in recent years and will be increasingly so in the near future.However, it is since 1956 that AI has been studying the emulation of intelligence by machines, understood as software and in some cases hardware. AI arose from the idea of building machines that-inspired by processes related to human intelligence-are able to solve complex problems, for which it is usually believed that some kind of intelligent reasoning is required. The main current area of AI research and application is machine learning (algorithms that learn and adapt based on the data they receive), which has found wide applications in recent years thanks to neural networks (mathematical models composed of artificial neurons), which in turn have enabled the emergence of deep learning (neural networks of greater complexity). Also belonging to the AI world are expert systems, computer vision, speech recognition, natural language processing, advanced robotics and some cybersecurity solutions. When it comes to AI there are those who are enthusiastic about it thinking of the opportunities, others are concerned as they fear futuristic technologies of a world where robots will replace humans, take away their jobs and make decisions for them. In reality, AI is already widely used in many fields, for example, in cell phones, smart objects (IoT), industries 4.0, for smart cities, cybersecurity systems, autonomous driving systems (drive or parking assistant), chat bots on various websites; these are just a few examples all based on typical artificial intelligence algorithms. Thanks to AI, companies can have a variety of advantages in providing advanced, personalized services, predicting trends, anticipating user choices, etc. But not all that glitters is gold: there are sometimes technical problems, ethical questions, security risks, and standards and legislation that are not entirely clear. Organizations already adopting AI-based solutions, or those planning to do so, could benefit from this publication to learn more about the opportunities, risks, and related countermeasures. Clusit's Community for Security hopes that this publication will provide readers with a useful overview of a reality, such as artificial intelligence, that will increasingly accompany us in our personal, social and working lives

Common data elements and data management: Remedy to cure underpowered preclinical studies

Lack of translation of data obtained in preclinical trials to clinic has kindled researchers to develop new methodologies to increase the power and reproducibility of preclinical studies. One approach relates to harmonization of data collection and analysis, and has been used for a long time in clinical studies testing anti-seizure drugs. EPITARGET is a European Union FP7-funded research consortium composed of 18 partners from 9 countries. Its main research objective is to identify biomarkers and develop treatments for epileptogenesis. As the first step of harmonization of procedures between laboratories, EPITARGET established working groups for designing project-tailored common data elements (CDEs) and case report forms (CRFs) to be used in data collection and analysis. Eight major modules of CRFs were developed, presenting >1000 data points for each animal. EPITARGET presents the first single-project effort for harmonization of preclinical data collection and analysis in epilepsy research. EPITARGET is also anticipating the future challenges and requirements in a larger-scale preclinical harmonization of epilepsy studies, including training, data management expertise, cost, location, data safety and continuity of data repositories during and after funding period, and incentives motivating for the use of CDEs

A digital teleneuropsychology platform for the diagnosis of mild cognitive impairment: from concept to certification as a medical device

BackgroundInnovative digital solutions are shaping a new concept of dementia care, opening additional venues for prevention, diagnosis, monitoring and treatment. Hereby, we report the development of a tablet-based teleneuropsychology platform (Tenepsia (R)), from concept to certification as Medical Device (MD) Class IIA, as per new MD regulation 745/2017.MethodsThe platform was designed for the remote cognitive evaluation and created thanks to the effort of a collaborative working group including experts from three Italian scientific societies and Biogen Italia S.r.l. (hereafter "Biogen"), and developers from Xenia Reply and Inside AI. The development strategy was guided by converting traditional paper-and-pencil tests into digital versions while maintaining comparable neuropsychological features and optimizing patient accessibility and user experience. The experts focused on the choice and adaptation of traditional neuropsychology measures for a 45-min teleneuropsychology assessment.ResultsThe developers created a web and a mobile interface, respectively, for the professional (neuropsychologist) and non-professional (patient and caregiver) use. Recording of voice, drawing and typing information was enabled. Instant dashboards provide a quick overview of the patient's condition. Simulation activities were performed to obtain MD certification, valid across Europe.ConclusionNeuropsychology services will benefit from the implementation in clinics of harmonized digital tools with adequate scientific and technological standards. The use of digital cognitive testing for the diagnosis of mild cognitive impairment is expected to enhance patient and clinician outcomes through simplified, digital objective data collection, sparing of time and resources, with a positive impact on healthcare costs and access to treatments, reducing inequalities and delays in diagnosis and cure

Prevalence, Characteristics, Risk Factors, and Outcomes of Invasively Ventilated COVID-19 Patients with Acute Kidney Injury and Renal Replacement Therapy

Background: There is no information on acute kidney injury (AKI) and continuous renal replacement therapy (CRRT) among invasively ventilated coronavirus disease 2019 (COVID-19) patients in Western healthcare systems. Objective: To study the prevalence, characteristics, risk factors and outcome of AKI and CRRT among invasively ventilated COVID-19 patients. Methods: Observational study in a tertiary care hospital in Milan, Italy. Results: Among 99 patients, 72 (75.0%) developed AKI and 17 (17.7%) received CRRT. Most of the patients developed stage 1 AKI (33 [45.8%]), while 15 (20.8%) developed stage 2 AKI and 24 (33.4%) a stage 3 AKI. Patients who developed AKI or needed CRRT at latest follow-up were older, and among CRRT treated patients a greater proportion had preexisting CKD. Hospital mortality was 38.9% for AKI and 52.9% for CRRT patients. Conclusions: Among invasively ventilated COVID-19 patients, AKI is very common and CRRT use is common. Both carry a high risk of in-hospital mortality