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The study of neutron-induced reactions is of high relevance in a wide variety of fields, ranging from stellar nucleosynthesis and fundamental nuclear physics to applications of nuclear technology. In nuclear energy, high accuracy neutron data are needed for the development of Generation IV fast reactors and accelerator driven systems, these last aimed specifically at nuclear waste incineration, as well as for research on innovative fuel cycles. In this context, a high luminosity Neutron Time Of Flight facility, n_TOF, is operating at CERN since more than a decade, with the aim of providing new, high accuracy and high resolution neutron cross-sections. Thanks to the features of the neutron beam, a rich experimental program relevant to nuclear technology has been carried out so far. The program will be further expanded in the near future, thanks in particular to new highflux experimental area, now under construction.

Les méthodes de réduction d'échelle statistiques sont des outils de post-traitement populaires qui sont largement utilisés dans de nombreux secteurs pour adapter les résultats biaisés à résolution grossière des simulations climatiques mondiales à l'échelle régionale à locale généralement requise par les utilisateurs. Ils vont de méthodes simples et pragmatiques de correction de biais (BC), qui ajustent directement les sorties du modèle d'intérêt (par exemple les précipitations) en fonction des observations locales disponibles, à des méthodes plus complexes de pronostic parfait (PP), qui dérivent indirectement des prédictions locales (par exemple les précipitations) à partir de variables appropriées du modèle à grande échelle de l'air supérieur (prédicteurs). Les méthodes de réduction d'échelle statistiques ont été largement utilisées et évaluées de manière critique dans les applications du changement climatique ; cependant, leurs avantages et leurs limites dans les prévisions saisonnières ne sont pas encore bien compris. En particulier, un problème clé dans ce contexte est de savoir s'ils servent à améliorer la qualité/compétence prévisionnelle des résultats des modèles bruts au-delà de l'ajustement de leurs biais systématiques. Dans cet article, nous analysons ce problème en appliquant deux méthodes BC et deux méthodes PP à la pointe de la technologie pour réduire les précipitations à partir d'un rétroprojecteur saisonnier multimodèle dans une région tropicale difficile, les Philippines. Pour évaluer correctement la valeur ajoutée potentielle au-delà de la réduction des biais du modèle, nous considérons deux scores de validation qui ne sont pas sensibles aux changements de la moyenne (catégories de corrélation et de fiabilité). Nos résultats montrent que, alors que les méthodes BC maintiennent ou aggravent la compétence des prévisions du modèle brut, les méthodes PP peuvent apporter une amélioration significative des compétences (aggravation) dans les cas où les variables prédictives à grande échelle considérées sont meilleures (pires) prédites par le modèle que les précipitations. Par exemple, les méthodes PP augmentent (diminuent) la fiabilité du modèle dans près de 40 % des stations considérées en été boréal (automne). Par conséquent, le choix d'une approche pratique de réduction d'échelle (BC ou PP) dépend de la région et de la saison. Los métodos de reducción de escala estadística son herramientas populares de posprocesamiento que se utilizan ampliamente en muchos sectores para adaptar los resultados sesgados de resolución gruesa de las simulaciones climáticas globales a la escala regional a local que generalmente requieren los usuarios. Van desde métodos de corrección de sesgo (BC) simples y pragmáticos, que ajustan directamente los resultados del modelo de interés (por ejemplo, precipitación) de acuerdo con las observaciones locales disponibles, hasta métodos de pronóstico perfecto (PP) más complejos, que derivan indirectamente predicciones locales (por ejemplo, precipitación) de variables apropiadas del modelo a gran escala del aire superior (predictores). Los métodos estadísticos de reducción de escala se han utilizado ampliamente y se han evaluado críticamente en aplicaciones de cambio climático; sin embargo, sus ventajas y limitaciones en el pronóstico estacional aún no se comprenden bien. En particular, un problema clave en este contexto es si sirven para mejorar la calidad/habilidad de pronóstico de los resultados del modelo bruto más allá del ajuste de sus sesgos sistemáticos. En este documento analizamos este problema aplicando dos métodos BC y dos PP de última generación para reducir la precipitación de un retroceso estacional multimodelo en una región tropical desafiante, Filipinas. Para evaluar adecuadamente el valor añadido potencial más allá de la reducción de los sesgos del modelo, consideramos dos puntuaciones de validación que no son sensibles a los cambios en la media (categorías de correlación y fiabilidad). Nuestros resultados muestran que, mientras que los métodos BC mantienen o empeoran la habilidad de los pronósticos del modelo en bruto, los métodos PP pueden producir una mejora significativa de la habilidad (empeoramiento) en los casos en que las variables predictoras a gran escala consideradas son mejores (peores) predichas por el modelo que la precipitación. Por ejemplo, se encuentra que los métodos PP aumentan (disminuyen) la confiabilidad del modelo en casi el 40% de las estaciones consideradas en el verano boreal (otoño). Por lo tanto, la elección de un enfoque de reducción de escala conveniente (ya sea BC o PP) depende de la región y la temporada. Statistical downscaling methods are popular post-processing tools which are widely used in many sectors to adapt the coarse-resolution biased outputs from global climate simulations to the regional-to-local scale typically required by users. They range from simple and pragmatic Bias Correction (BC) methods, which directly adjust the model outputs of interest (e.g. precipitation) according to the available local observations, to more complex Perfect Prognosis (PP) ones, which indirectly derive local predictions (e.g. precipitation) from appropriate upper-air large-scale model variables (predictors). Statistical downscaling methods have been extensively used and critically assessed in climate change applications; however, their advantages and limitations in seasonal forecasting are not well understood yet. In particular, a key problem in this context is whether they serve to improve the forecast quality/skill of raw model outputs beyond the adjustment of their systematic biases. In this paper we analyze this issue by applying two state-of-the-art BC and two PP methods to downscale precipitation from a multimodel seasonal hindcast in a challenging tropical region, the Philippines. To properly assess the potential added value beyond the reduction of model biases, we consider two validation scores which are not sensitive to changes in the mean (correlation and reliability categories). Our results show that, whereas BC methods maintain or worsen the skill of the raw model forecasts, PP methods can yield significant skill improvement (worsening) in cases for which the large-scale predictor variables considered are better (worse) predicted by the model than precipitation. For instance, PP methods are found to increase (decrease) model reliability in nearly 40% of the stations considered in boreal summer (autumn). Therefore, the choice of a convenient downscaling approach (either BC or PP) depends on the region and the season. طرق تقليص النطاق الإحصائي هي أدوات شائعة لما بعد المعالجة تستخدم على نطاق واسع في العديد من القطاعات لتكييف المخرجات المتحيزة ذات الدقة الخشنة من محاكاة المناخ العالمي إلى النطاق الإقليمي إلى المحلي المطلوب عادةً من قبل المستخدمين. وهي تتراوح من طرق بسيطة وعملية لتصحيح التحيز (BC)، والتي تعدل بشكل مباشر مخرجات النموذج محل الاهتمام (مثل هطول الأمطار) وفقًا للملاحظات المحلية المتاحة، إلى طرق التكهن المثالي (PP) الأكثر تعقيدًا، والتي تستمد بشكل غير مباشر التنبؤات المحلية (مثل هطول الأمطار) من متغيرات النموذج المناسبة واسعة النطاق في الهواء العلوي (التنبؤات). تم استخدام طرق تقليص النطاق الإحصائي على نطاق واسع وتقييمها بشكل نقدي في تطبيقات تغير المناخ ؛ ومع ذلك، فإن مزاياها وقيودها في التنبؤ الموسمي ليست مفهومة جيدًا بعد. على وجه الخصوص، تتمثل المشكلة الرئيسية في هذا السياق في ما إذا كانت تعمل على تحسين جودة/مهارة التنبؤ لمخرجات النموذج الخام بما يتجاوز تعديل تحيزاتها المنهجية. في هذه الورقة، نقوم بتحليل هذه المشكلة من خلال تطبيق طريقتين حديثتين قبل الميلاد وطريقتين للبولي بروبلين لتقليل هطول الأمطار من توقعات موسمية متعددة النماذج في منطقة استوائية صعبة، الفلبين. لتقييم القيمة المضافة المحتملة بشكل صحيح بما يتجاوز الحد من تحيزات النموذج، نأخذ في الاعتبار درجتي التحقق غير الحساستين للتغيرات في المتوسط (فئتي الارتباط والموثوقية). تظهر نتائجنا أنه في حين أن طرق BC تحافظ على مهارة تنبؤات النموذج الخام أو تزيدها سوءًا، فإن طرق PP يمكن أن تسفر عن تحسن كبير في المهارات (تدهور) في الحالات التي تكون فيها المتغيرات التنبؤية واسعة النطاق التي يعتبرها النموذج أفضل (أسوأ) من هطول الأمطار. على سبيل المثال، تم العثور على طرق PP لزيادة (تقليل) موثوقية النموذج في ما يقرب من 40 ٪ من المحطات التي يتم النظر فيها في الصيف الشمالي (الخريف). لذلك، يعتمد اختيار نهج تصغير النطاق المناسب (إما BC أو PP) على المنطقة والموسم.

Sea turtles are an iconic group of marine megafauna that have been exposed to multiple anthropogenic threats across their different life stages, especially in the past decades. This has resulted in population declines, and consequently many sea turtle populations are now classified as threatened or endangered globally. Although some populations of sea turtles worldwide are showing early signs of recovery, many still face fundamental threats. This is problematic since sea turtles have important ecological roles. To encourage informed conservation planning and direct future research, we surveyed experts to identify the key contemporary threats (climate change, direct take, fisheries, pollution, disease, predation, and coastal and marine development) faced by sea turtles. Using the survey results and current literature, we also outline knowledge gaps in our understanding of the impact of these threats and how targeted future research, often involving emerging technologies, could close those gaps.

In the fission yeast Schizosaccharomyces pombe, the stationary phase-specific transcription factor Phx1 contributes to long-term survival, stress tolerance, and meiosis. We identified Phx1-dependent genes through transcriptome analysis, and further analyzed those related with carbohydrate and thiamine metabolism, whose expression decreased in ∆phx1. Consistent with mRNA changes, the level of thiamine pyrophosphate (TPP) and TPP-utilizing pyruvate decarboxylase activity that converts pyruvate to acetaldehyde were also reduced in the mutant. Therefore, Phx1 appears to shift metabolic flux by diverting pyruvate from the TCA cycle and respiration to ethanol fermentation. Among the four predicted genes for pyruvate decarboxylase, only the Phx1-dependent genes (pdc201+ and pdc202+) contributed to long-term survival as judged by mutation and overexpression studies. These findings indicate that the Phx1-mediated long-term survival is achieved primarily through increasing the synthesis and activity of pyruvate decarboxylase. Consistent with this hypothesis, we observed that Phx1 curtailed respiration when cells entered stationary phase. Introduction of Δphx1 mutation compromised the long-lived phenotypes of Δpka1 and Δsck2 mutants that are devoid of pro-aging kinases of nutrient-signalling pathways, and of the Δpyp1 mutant with constitutively activated stress-responsive kinase Sty1. Therefore, achievement of long-term viability through both nutrient limitation and anti-stress response appears to be dependent on Phx1.

Intermediate band materials incorporate a collection of energy levels with special optoelectronic properties within the semiconductor bandgap. This feature broadens the energy range of the solar spectrum useful for photovoltaic conversion and has the potential for enabling both high-current and high-voltage photovoltaic cells. Here we present our preliminary results on a novel intermediate band solar cell based on creating an intermediate band through the incorporation of a large concentration of Ti atoms in a GaAs crystal. The characterization of the material verifies a high concentration of incorporated Ti and the absence of structural defects. The cells show below-bandgap photon absorption with a likely contribution from As antisites and Ga vacancies. The initially degraded open-circuit voltage of the cells exhibits a high voltage recovery from 0.1 V (at room temperature and one-sun irradiance conditions) to 1.4 V (at low temperature and concentrated light).

Abstract Two different strains of microalgae, one raphidophyte and one dinoflagellate, were tested under different abiotic conditions with the goal of enhancing lipid production. Whereas aeration was crucial for biomass production, nitrogen deficiency and temperature were found to be the main abiotic parameters inducing the high-level cellular accumulation of neutral lipids. Net neutral lipid production and especially triacylglycerol (TAG) per cell were higher in microalgae (>200% in Alexandrium minutum, and 30% in Heterosigma akashiwo) under treatment conditions (25°C; 330 μM NaNO3) than under control conditions (20°C; 880 μM NaNO3). For both algal species, oil production (free fatty acids plus TAG fraction) was also higher under treatment conditions (57 mg L−1 in A. minutum and 323 mg L−1 in H. akashiwo). Despite the increased production and accumulation of lipids in microalgae, the different conditions did not significantly change the fatty acids profiles of the species analyzed. These profiles consisted of saturated fatty acids (SAFA) and polyunsaturated fatty acids (PUFA) in significant proportions. However, during the stationary phase, the concentrations per cell of some PUFAs, especially arachidonic acid (C20:4n6), were higher in treated than in control algae. These results suggest that the adjustment of abiotic parameters is a suitable and one of the cheapest alternatives to obtain sufficient quantities of microalgal biomass, with high oil content and minimal changes in the fatty acid profile of the strains under consideration.