Motivated by the Blackorby-Schworm (1993) observation that market outcomes may differ from those originating in market-actor optimization, this paper claims that the number of banks in the market is larger than the number justified by bank profit maximization alone or in combination with bank depositor welfare maximization. This claim is made within the context of bilateral monopoly banks and intertemporal utility maximization by bank depositors. The basic policy implication towards bank population rationalization is a minimization of the deviation away from the optimal interest rate margin at every stage of the business cycle. It is meant to be an acyclical policy though the target of optimal bank population is attainable by active countercyclical policy as well. The nature of this policy issue makes the use of macroprudential measures imperative, jointly perhaps with a fiscal-monetary policy mix. A dynamic version of the model in a Cournot environment is akin to the modeling of Minsky's hypothesis of financial fragility.

Rental unaffordability is defined as spending more than 30% of a household’s gross income on rent. Post-pandemic inflation and interest rate increases have intensified rental unaffordability. This research examines rental affordability in the U.S., the U.K., and Canada. It also explores the effect of renters’ “affordability knowledge” – defined as the expertise tenants have and use to make economical rental housing choices – on rent expenditure and affordability positioning and compares personal finances, economic perspectives, and demographics based on renters in affordable and unaffordable situations. The results show that nearly two-thirds of the renters studied are in unaffordable rental situations. Interestingly, affordability knowledge was found to reduce rent spent and increase affordability situations. Significant demographic differences were found between those in affordable and unaffordable rental situations, including rent spending, food spending, transportation spending, savings, perceived homeownership likelihood, and age. The research offers important insight into current rental affordability economics, recommendations for policymakers, and opportunities for real estate organizations.

Competitive balance indicates the degree of control participating teams have over a sports competition. Supporters look for excuses to justify their team’s defeat and the triumph of their rivals. If the competition has required a preliminary qualifying group stage, they will argue that the winning team was in an "easy group" from the start, and their team was unlucky to be in a "difficult group". It is therefore of interest to determine what is an "easy group" and what is a "difficult group". This is directly related to the concept of competitive balance. We have a wide range of indices to measure competitive balance. We will use the Distance to Competitive Balance, a standardized index that complies cardinality property. The perfectly unbalanced distribution is the truncated cascade, which allows the maximum value of concentration to be obtained. We focus our attention on the UEFA Champions League, before and after competition, and we measure the competitive balance of the qualifying stage groups between the 1999/2000 and 2022/2023 seasons. The composition of the UEFA Champions League groups seems to be balanced and has no influence on which team will be the champion. A highly competitive group will be more "difficult" in terms of qualifying than a highly concentrated one. Supporters say that their team was unlucky to be in a “difficult” group, but the data does not prove them right.

Nanomaterials have shown great potential in anti-tumor applications and are currently the focus of research. This review article aims to provide a comprehensive overview of the challenges encountered in oncology treatment and how nanomaterials are being utilized to overcome these obstacles. The authors discuss the limitations of conventional treatments, including limited efficacy, side effects, and toxicity issues. They highlight the importance of early tumour diagnosis and personalized treatment plans, as well as the need for innovative therapeutic approaches such as targeted therapy, immunotherapy, and gene therapy. The article primarily focuses on how nanomaterials can be engineered to achieve specific recognition and aggregation within tumour tissues through surface modifications involving targeting molecules such as antibodies, peptides, and receptor ligands. This surface modification technique facilitates improved targeting in the targeting of photodynamic therapy, while minimizing harm to normal tissues. The authors also discuss the potential and future prospects of nanomaterials in tumour therapy, including breakthroughs in their application, biosafety concerns, biocompatibility issues, preparation processes, clinical translation challenges, interdisciplinary cooperation, international exchange, relevant regulations and ethical guidelines. Overall, this review highlights the substantial potential of nanomaterials in oncology treatment, emphasizing the need for careful consideration of safety concerns to ensure their safe and effective application. The authors conclude that strengthening interdisciplinary cooperation and international exchange will contribute to the healthy development of nanomaterials in oncology treatment.

Parkinson’s Disease (PD) is a prevalent progressive neurodegenerative condition affecting millions globally. Research has found that individuals with PD have a reduced risk of certain cancers, such as colon, lung, and rectal cancers, but an increased risk of brain cancer. Therefore, there is an urgent need for the development of advanced PD diagnostic methods and for investigating the relationships between risk factors, such as lifestyle due to handedness associated with various types of cancers. Recent ad- vancements in magnetic resonance imaging have enhanced PD diagnosis, reducing misdiagnosis and facilitating more accurate disease progression monitoring. Nevertheless, challenges exist, particularly in the distinction of PD between left-handed and right-handed patients over time. This survey provides an overview of contemporary deep learning-based imag- ing analysis methodologies, encompassing both non-longitudinal and lon- gitudinal contexts. We also explore existing limitations and prospects for refinement to gain deeper insights. These insights are poised to inform the development of personalized treatment strategies for PD patients while elucidating the current disparities between deep learning models and their efficacious implementation in clinical practice.