The development of Photovoltaic (PV) systems operating under very high solar concentration has attracted considerable interest and motivated a significant amount of research and development over the past two decades, driven by the prospect of achieving exceptionally high solar-to-electricity conversion efficiency, and, ultimately, lowering the cost of solar electricity. Although this field has experienced a decline in recent years, mainly due to the rapid expansion of conventional PV, conversion under ultra-high solar flux (≥1000 suns) remains a subject of singular interest that stems from the interplay of diverse physical, optical, and thermal phenomena, the significant technical challenges inherent to its practical implementation, and its direct relevance to several high-efficiency PV cell technologies currently under development. In this article, we review the physical motivations and principles underlying the operation of PV cells under ultra-high solar concentration. We describe the limiting mechanisms that can significantly affect the performance of PV cells under such extreme conditions, before listing and analysing the strategies proposed to limit their effects. Finally, we review and discuss the emerging high-efficiency PV cell technologies and provide practical recommendations grounded in two decades of ultra-high concentration PV research.
The present study provides a novel methodology to quantify ESG, using deep machine learning along with earnings conference calls, to examine the extent to which sentiment patterns around ESG anchor words convey information about future stock crash risk. We define ESG anchor words as terms related to the three pillars of ESG, with the sentiment surrounding these words revealing firms' disclosure strategies and information transparency. As Bordalo et al. (Q J Econ 135:1399–1442, 2020) show, an anchor may relate to higher attention being paid by an observer. In specific, they show that when a risk surpasses a known threshold, identified as a risk-anchor, people seem to pay more attention. For the task in hand, we first compile expanded dictionaries of ESG-related terms using word2vec and then employ FinBERT, to extract sentiment from sentences containing these ESG anchor words. Using stock crash risk as our primary measure of information asymmetry and bad news hoarding, our results show that environmental sentiment operates as a credible signal that reduces crash risk, with effects amplified in environmentally sensitive industries and attenuated by financial opacity; social sentiment exhibits context-dependent patterns, increasing crash risk in optimistic narratives but reducing it in balanced communications, while governance sentiment shows low impact. Our findings likely imply that not all ESG communication reduces information asymmetry; credibility depends critically on dimension-specific materiality, narrative consistency and financial transparency.
The present study aimed to explore the complex interactions between the Interpersonal Competence -Sense of Relatedness- which is a specific dimension of resiliency, perceived relationship quality (with both parents and teachers), and depressive symptomatology, in children and preadolescents with and without Special Educational Needs (SEN). The study sample of 465 Greek elementary school and Junior High school students (age range 10–14) and their primary teachers, randomly selected from public schools in three prefectures of Crete, with160 of them facing SEN. Participants were administered: (a) Sense of Relatedness Scale of the Resiliency Scales for Children Adolescents (RSCA) (b) Parental Acceptance-Rejection Questionnaire - Short Form (Child PARQ mother father) (c) Teacher Acceptance-Rejection Questionnaire (TARQ) (d) Children’s Depression Inventory (CDI) and e) Teacher’s Assessment Questionnaire (TEACH) for students’ school functioning and behavioural/emotional screening. Multiple Regression Exploratory Analyses, Confirmatory Factor Analyses (CFA), and Path Analyses were conducted. Results highlighted the impact of the perceived relationship quality with the father, which appeared as more important than the mother’s impact on students’ depressive affect. Additionally, depressive symptoms of students with SEN were associated with perceived acceptance/rejection by both parents and teachers as compared with those without SEN. Results were discussed in terms of the need for prevention and intervention programs focusing on children’s resiliency as well as in valuing and encouraging fatherhood. The aim of the present study was to explore the complex interactions between perceived relationship quality (with fathers, mothers and teachers), through perceptions of Acceptance/Rejection, Sense of Relatedness as dimension of Resiliency and depression symptomatology in students with and without Special Educational Needs (SEN). For students without SEN, the father-child relationship was associated with depression symptoms, as well as to all Interpersonal Competence Resiliency factors. For students with SEN the pathways leading to depression symptoms were from perceived rejection from the father, mother and the teacher, mediated by Resiliency dimensions (of Trust and Support).
A novel modular multilevel converter (MMC)-based spark-gap trigger generator for high-voltage pulsed-power applications has been developed and presented in this work. It fully exploits the inherent modularity of MMC topology to generate high-voltage trigger pulses in a flexible and scalable manner. A prototype based on insulated gate bipolar transistors (IGBTs) was constructed to effectively trigger the breakdown of the spark gaps of a Marx Bank consisting of four capacitors charged to 50 kV. It is characterized by a fast rise time and produces pulses of 15 kV with a duration of similar to 200 ns. Using semiconductors and foil capacitors, the new trigger generator successfully replaces the thyratron-based generator.
Material extrusion-based additive manufacturing (MEXAM) has emerged as a transformative technology for ultra-performance polymers (UPPs) and high-performance polymers (HPPs), enabling their use in demanding applications across diverse industries such as aerospace, automotive, medical, and defense. Their high strength-to-weight ratio, heat resistance, chemical stability, and performance retention under harsh conditions perfectly match the high potential of additive manufacturing for cost-effectiveness, flexibility, and adaptability. Among the most studied UPPs/HPPs, Polyimide (PΙ), polyetherketoneketone (PEKK), and polyetheretherketone (PEEK) have gained substantial attention due to their printability and superior functional properties. Despite these advantages, MEXAM of UPPs and HPPs presents considerable challenges. This review provides a comprehensive analysis of the molecular, rheological, thermal, and structural characteristics of UPPs/HPPs and their major composites that influence their printability and performance. A comparative evaluation of their advantages and limitations is presented, along with a discussion on recent advancements in process optimization. Research efforts for the optimization of MEXAM process control parameters were reviewed and interpreted. Furthermore, this work explores the integration of Artificial Intelligence (AI)-assisted optimization strategies to enhance processing efficiency and material properties. This study identifies key research gaps and highlights opportunities for future advancements in the field of MEXAM for UPPs and HPPs.
