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Cholera remains a global public health threat in regions where social vulnerabilities intersect with climate and weather processes that impact infectious Vibrio cholerae . While access to safe drinking water and sanitation facilities limit cholera outbreaks, sheer cost of building such infrastructure limits the ability to safeguard the population. Here, using Yemen as an example where cholera outbreak was reported in 2016, we show how predictive abilities for forecasting risk, employing sociodemographical, microbiological, and climate information of cholera, can aid in combating disease outbreak. An epidemiological analysis using Bradford Hill Criteria was employed in near-real-time to understand a predictive model’s outputs and cholera cases in Yemen. We note that the model predicted cholera risk at least four weeks in advance for all governorates of Yemen with overall 72% accuracy (varies with the year). We argue the development of anticipatory decision-making frameworks for climate modulated diseases to design intervention activities and limit exposure of pathogens preemptively.

In the humanitarian sector, anticipatory action entails acting ahead of predicted hazardous events to prevent or mitigate potential impacts and needs. It leverages early warnings to bridge preparedness and response, with a core principle being the provision of ex-ante emergency funding for preagreed early actions. Traditionally applied to extreme climatic events, there is growing interest in integrating anticipatory action into disease outbreak preparedness and response. We present an analytical framework for trigger development for climate-sensitive infectious disease outbreaks based on a review of existing and emerging practices from the Red Cross Red Crescent Movement, United Nations agencies and Médecins Sans Frontières since 2014. We propose that, depending on data availability, there are four broad approaches for trigger development. First, the humanitarian sector could scale up the release of prearranged funding based on real-time surveillance data (eg, suspected cases) while other emergency funding is secured. Second, the humanitarian sector could take advantage of weather forecasts and seasonal climate forecasts to anticipate outbreaks linked to extreme climatic events, anomalous climatic conditions or highly suitable climatic conditions. Third, to extend the lead time available for intervention, the humanitarian sector could use observed environmental and socioeconomic transmission risk factors (eg, population displacement, overcrowding, presence of vectors, weather changes) in combination with real-time surveillance data to improve early detection or curb a rapid increase in cases, while other emergency funding is secured. Fourth, data-driven outbreak forecasting using seasonal forecasts can help extend the lead time further to make informed decisions about future risks. We present examples and discuss the trade-offs between approaches. As anticipatory action for outbreaks becomes established, we expect that future applications will integrate all four approaches.

Trioza erytreae (Del Guercio, 1918) (Hemiptera: Triozidae) is a citrus pest which produces gall symptoms on leaves and transmits bacteria associated with the citrus disease Huanglongbing, ‘Candidatus Liberibacter’ spp. In the present work, the biology and behaviour of T. erytreae were studied in different rootstock–cultivar combinations. Six rootstocks were used, Flying dragon (FD), ‘Cleopatra’ mandarin (CL), Carrizo citrange (CC), Forner-Alcaide no.5 (FA5), Forner-Alcaide no.517 (FA517) and Citrus macrophylla (CM), and six scion cultivars: ‘Star Ruby’, ‘Clemenules’, ‘Navelina’, ‘Valencia Late’, ‘Fino 49’ and ‘Ortanique’. Survival and oviposition were evaluated in a no-choice trial, and preference in a choice trial, all of them under greenhouse conditions. Trioza erytreae did not show a clear settle preference for any citrus combination. However, it was able to lay more eggs in ‘Fino 49’ grafted on CC than on FD. In terms of survival, ‘Ortanique’ grafted onto FA5 was more suitable than when grafted onto FA517, and in the case of ‘Valencia Late’, when it was grafted onto CM rather than CC. Our results showed that T. erytreae behave differently depending on the citrus combination.

This chapter explains the process which involves building a simple but fast face recognition system using computer vision and image processing techniques. The authors have developed a face recognition using a feature-based method. To perform the most robust geometrical face recognition, they use 31 different facial features. These features are calculated from the different geometrical regions located in the segmentation stage using an improvement of K-means clustering algorithm. Therefore, the input to the system is a single photograph of the face of the person who wants to be authenticated by the system; whereas the output of the system will depend on the application that they give to the system. In identification problems, the system will give back the determined identity from a database of known individuals, but in verification problems, the system will confirm or reject the claimed identity of the input face. Finally, in the future work section, the authors point out the way to improve the face recognition to build a more robust security system.

Nowadays, mobile communications are experiencing a strong growth, being more and more indispensable. One of the key issues in the design of mobile networks is the frequency assignment problem (FAP). This problem is crucial at present and will remain important in the foreseeable future. Real-world instances of FAP typically involve very large networks, which can be handled only by heuristic methods. In the present work, we are interested in optimizing frequency assignments for problems described in a mathematical formalism that incorporates actual interference information, measured directly on the field, as is done in current GSM networks. To achieve this goal, a range of metaheuristics have been designed, adapted, and rigourously compared on two actual GSM networks modeled according to the latter formalism. To generate quickly and reliably high-quality solutions, all metaheuristics combine their global search capabilities with a local-search method specially tailored for this domain. The experiments and statistical tests show that in general, all metaheuristics are able to improve upon results published in previous studies, but two of the metaheuristics emerge as the best performers: a population-based algorithm (Scatter Search) and a trajectory based (1+1) Evolutionary Algorithm. Finally, the analysis of the frequency plans obtained offers insight about how the interference cost is reduced in the optimal plans.

Background/Objectives: Like many other countries, the management of pancreatic cancer in Spain has developed in a fragmented manner. This study analyzes clinical outcomes related to patient volume at different centers after left pancreatectomy (LP). Our goal is to determine whether our practices align with the standards established in the literature and assess whether centralization’s advantages significantly outweigh its disadvantages. Methods: The SPANDISPAN Project (SPANish DIStal PANcreatectomy) is an observational, prospective, multicenter study focused on LP conducted in Spanish Hepato-Pancreato-Biliary (HPB) Surgery Units from 1 February 2022 to 31 January 2023. HPB units were defined as high volume if they performed more than 10 LPs annually. Results: This study included 313 patients who underwent LP at 42 centers across Spain over the course of a year. A total of 40.3% of the procedures were performed in high-volume centers. Significant differences in preoperative variables were only observed in ASA scores, which were higher in the high-volume group. Intraoperatively, minimally invasive surgical techniques were performed more frequently in high-volume centers. Postoperatively, the administration of somatostatin, major complications, and B and C postoperative pancreatic fistula (POPF) were more frequent in low-volume hospitals. Conclusions: The findings revealed that high-volume centers had a higher rate of minimally invasive surgery, lower intraoperative bleeding, fewer complications, and reduced POPFs compared to low-volume centers. However, it is important to note that low-volume centers still demonstrated acceptable outcomes. Thus, the selective referral of more complex laparoscopic procedures could initiate a gradual centralization of surgical practices.