Explore the vast range of titles available.

PurposeTo perform a systematic search and review of the available literature on the learning curves (LCs) in laparoscopic and robot-assisted prostate surgery.MethodsMedline was systematically searched from 1946 to January 2021 to detect all studies in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement, reporting on the LC in laparoscopic radical prostatectomy (LRP), laparoscopic simple prostatectomy (LSP), robot-assisted radical prostatectomy (RARP) and robot-assisted simple prostatectomy (RSP).ResultsIn total, 47 studies were included for qualitative synthesis evaluating a single technique (LRP, RARP, LSP, RSP; 45 studies) or two techniques (LRP and RARP; 2 studies). All studies evaluated outcomes on real patients. RARP was the most widely investigated technique (30 studies), followed by LRP (17 studies), LSP (1 study), and RSP (1 study). In LRP, the reported LC based on operative time; estimated blood loss; length of hospital stay; positive surgical margin; biochemical recurrence; overall complication rate; and urinary continence rate ranged 40–250, 80–250, 58–200, 50–350, 110–350, 55–250, 70–350 cases, respectively. In RARP, the corresponding ranges were 16–300, 20–300, 25–200, 50–400, 40–100, 20–250, 30–200, while LC for potency rates was 80–90 cases.ConclusionsThe definition of LC for laparoscopic and robot-assisted prostate surgery is not well defined with various metrics used among studies. Nevertheless, LCs appear to be steep and continuous. Implementation of training programs/standardization of the techniques is necessary to improve outcomes.

As industries come under growing pressure to minimize carbon emissions without compromising the efficiency of operations, the integration of energy-aware production scheduling with emerging energy markets, renewable energy, and policy mechanisms is critical. This paper identifies critical shortcomings in current academic and industrial approaches—namely, an excessive reliance on deterministic assumptions, a limited focus on dynamic operational realities, and the underutilization of regulatory mechanisms such as carbon trading. We advocate for a paradigm shift to more robust, adaptable, and policy-compliant scheduling systems that provide space for on-site renewable generation, battery energy storage systems (BESSs), demand-response measures, and real-time electricity pricing schemes like time-of-use (TOU) and real-time pricing (RTP). By integrating recent advances and their critical analysis of limitations, we map out a future research agenda for the integration of uncertainty modeling, machine learning, and multi-level optimization with policy compliance. In this paper, we propose the need for joint efforts from researchers, industries, and policymakers to collectively develop industrial scheduling systems that are both technically efficient and adherent to sustainability and regulatory requirements.

BackgroundCOVID-19 pandemic is a challenge that the world had never encountered in the last 100 years. In order to mitigate its negative effects, governments worldwide took action by prohibiting at first certain activities and in some cases by a countrywide lockdown. Greece was among the countries that were struck by the pandemic. Governmental authorities took action in limiting the spread of the pandemic through a series of countermeasures, which built up to a countrywide lockdown that lasted 42 days.MethodologyThis research aims at identifying the effect of certain socioeconomic factors on the travel behaviour of Greek citizens and at investigating whether any social groups were comparatively less privileged or suffered more from the lockdown. To this end, a dynamic online questionnaire survey on mobility characteristics was designed and distributed to Greek citizens during the lockdown period, which resulted in 1,259 valid responses. Collected data were analysed through descriptive and inferential statistical tests, in order to identify mobility patterns and correlations with certain socioeconomic characteristics. Additionally, a Generalised Linear Model (GLM) was developed in order to examine the potential influence of socioeconomic characteristics to trip frequency before and during the lockdown period.ResultsOutcomes indicate a decisive decrease in trip frequencies due to the lockdown. Furthermore, the model’s results indicate significant correlations between gender, income and trip frequencies during the lockdown, something that is not evident in the pre-pandemic era.

•Optimal inventory profile and flow decisions for the COVID-19 Vaccine Supply Chain.•Optimization of daily vaccination plans in the clinics.•Development of a novel mixed-integer linear programming model.•A decomposition algorithm to successfully address large-scale problems.•Reactive planning of vaccinations through a rolling-horizon technique. This work presents a novel framework to simultaneously address the optimal planning of COVID-19 vaccine supply chains and the optimal planning of daily vaccinations in the available vaccination centres. A new mixed integer linear programming (MILP) model is developed to generate optimal decisions regarding the transferred quantities between locations, the inventory profiles of central hubs and vaccination centres and the daily vaccination plans in the vaccination centres of the supply chain network. Specific COVID-19 characteristics, such as special cold storage technologies, limited shelf-life of mRNA vaccines in refrigerated conditions and demanding vaccination targets under extreme time pressure, are aptly modelled. The goal of the model is the minimization of total costs, including storage and transportation costs, costs related to fleet and staff requirements, as well as, indirect costs imposed by wasted doses. A two-step decomposition strategy based on a divide-and-conquer and an aggregation approach is proposed for the solution of large-scale problems. The applicability and efficiency of the proposed optimization-based framework is illustrated on a study case that simulates the Greek nationwide vaccination program. Finally, a rolling horizon technique is employed to reactively deal with possible disturbances in the vaccination plans. The proposed mathematical framework facilitates the decision-making process in COVID-19 vaccine supply chains into minimizing the underlying costs and the number of doses lost. As a result, the efficiency of the distribution network is improved, thus assisting the mass vaccination campaigns against COVID-19.

Scheduling is a major component for the efficient operation of the process industries. Especially in the current competitive globalized market, scheduling is of vital importance to most industries, since profit margins are miniscule. Prof. Sargent was one of the first to acknowledge this. His breakthrough contributions paved the way to other researchers to develop optimization-based methods that can address a plethora of process scheduling problems. Despite the plethora of works published by the scientific community, the practical implementation of optimization-based scheduling in industrial real-life applications is limited. In most industries, the optimization of production scheduling is seen as an extremely complex task and most schedulers prefer the use of a simulation-based software or manual decision, which result to suboptimal solutions. This work presents a comprehensive review of the theoretical concepts that emerged in the last 30 years. Moreover, an overview of the contributions that address real-life industrial case studies of process scheduling is illustrated. Finally, the major reasons that impede the application of optimization-based scheduling are critically analyzed and possible remedies are discussed.

What are the main findings? An XGBoost machine learning framework classifies bus delay severity at stops with high predictive accuracy. Meteorological and seasonal variables emerge as dominant predictors of delay severity, reflecting the influence of system-wide operating conditions. What are the implications of the main findings? Delay classification serves as a spatial diagnostic tool for identifying and ranking reliability hotspots along the network. These hotspots provide a clear basis for prioritizing targeted infrastructure upgrades at specific bus stops and corridor segments. Public transport reliability largely determines the performance of smart urban mobility systems, as it directly affects passenger satisfaction and network efficiency. However, the strategic planning of public transport infrastructure is often carried out without dynamic, data-driven insights into operational performance, instead relying solely on static historical records of network operations. This study develops a data-driven framework based on the XGBoost machine learning algorithm to support the prioritization of infrastructure interventions by predicting delay severity and identifying reliability hotspots along an urban bus route. Delay severity is categorized into three classes (minor, moderate, and severe), using a model that incorporates spatial, temporal, operational, and meteorological variables. The XGBoost framework achieves a high predictive performance, with classification accuracies of 91.5% and 89.7% for the outbound and inbound bus route directions, respectively. Feature importance analysis indicates that seasonal and meteorological variables are critical factors influencing delay severity, highlighting the role of broader external environmental conditions on corridor performance. Furthermore, spatial analysis identifies specific bus stops with high delay probabilities, indicating hotspots where infrastructure upgrades should be prioritized at the stop and corridor levels. This study proposes a decision-support tool that enables targeted infrastructure investments at locations where they are most needed, contributing to more efficient and resilient public transport systems in smart cities.

Under growing urbanization and environmental challenges, sustainable urban mobility has become a critical priority for cities worldwide. Public Transport (PT) systems play a central role in reducing car dependency, lowering emissions, increasing network capacity, and promoting more equitable and efficient access to urban spaces for all users. Hence, the present paper aims to investigate PT preferences in the city of Larissa, Greece. Larissa is a medium-sized city currently serviced only by buses, and is currently focusing on the potential introduction of a new tram system to operate in parallel with existing bus services. To this end, a SP survey was designed and implemented, resulting in 972 observations that were collected for further statistical analysis. Survey results show a slight preference for trams over buses, with 54.63% selecting the tram and 45.37% favoring the buses. Moreover, a context-based segmentation pipeline was established using PCA, DBSCAN and t-SNE algorithms, aiding the visualization of existing clusters for transport choice approaches. Afterwards, a series of mixed logit models was applied, and statistically significant variables influencing mode choice were determined. The study also examines Value of Time (VoT) metrics and finds that respondents assign lower VoTs to trams than to buses, especially in out-of-vehicle segments of the journey, such as waiting and walking, and therefore consider trams as more pleasant and less burdensome. The findings also indicate that passengers place a high value on the quality of infrastructure related to access and waiting times, underlining the need to improve the overall user experience beyond the vehicle itself. In summary, the present research offers valuable insights into how the introduction of a tram system could possibly reshape PT usage patterns when compared with the legacy existing bus services.

This paper investigates how the travel behavior relating to Public Transport (PT) changed during the COVID-19 pandemic, and which are the expectations about the extent of PT use post-pandemic. A revealed preferences questionnaire survey was distributed within an academic community in the city of Thessaloniki, Greece. To understand the factors potentially determining the future PT use, hierarchical ordered probit and bivariate ordered probit models were estimated. Results showed that the frequent PT users reduced by almost 75% during the pandemic. More than 29% of the local academic community members are reluctant to resume PT use at pre-pandemic levels. Non-captive users, teleworkers and those being unsatisfied with cleanliness and safety are less willing to travelling by PT post-pandemic. Female and under-stress passengers were found to particularly appreciate the use of facemasks and the increased service frequencies as post-pandemic policy measures. The study findings can inform the recovery strategies of transport authorities in order to retain the PT ridership at levels that will not threat the long-term viability of service provision. Future research may complement these findings by examining other population segments, such as the commuters and the elderly, under more advanced modelling techniques to account for additional unobserved behavioral patterns.

People make numerous trips every day for a variety of purposes. Transport mode choice directly impacts travel time, congestion, and environmental conditions. It also depends on various economic, social, environmental, and personal related factors. This paper investigates the association between identity characteristics and transport mode choices in Thessaloniki, Greece. A customized questionnaire survey was carried out with 506 individuals in 2019 to collect data on nine self-declared personal statuses (affiliation with environment, place of residence, career, companionship, etc.) and trip frequencies of all available transport options in Thessaloniki. We ran latent class analyses to uncover three identity clusters. The Active individuals prefer public transport over private car, and they are mostly young, sporty, and with low incomes. Additionally, the Family-Oriented individuals are comparatively older, and they have greater access to private cars and higher incomes, while the Typical Urban population exhibits a slightly higher use of cars and public transport than the Active one. Trips on foot and by car (as passengers) are equally preferred by all latent classes’ populations. Our findings highlight the role of individuals’ identities in the development of travel behavior and may assist with the design of targeted policies and marketing strategies, which will facilitate sustainable urban mobility behaviors.

This work addresses the lot-sizing and production scheduling problem of multi-stage multi-product food industrial facilities. More specifically, the production scheduling problem of the semi-continuous yogurt production process, for two large-scale Greek dairy industries, is considered. Production scheduling decisions are made using two approaches: (i) an optimization approach and (ii) a rule-based approach, which are followed by a comparative study. An MILP model is applied for the optimization of short-term production scheduling of the two industries. Then, the same problems are solved using the commercial scheduling tool ScheduleProTM, which derives scheduling decisions using simulation-based techniques and empirical rules. It is concluded that both methods, despite having their advantages and disadvantages, are suitable for addressing complex food industrial scheduling problems. The optimization-based approach leads to better results in terms of operating cost reduction. On the other hand, the complexity of the problem and the experience of production engineers and plant operators can significantly impact the quality of the obtained solutions for the rule-based approach.