STEM Students’ Perceptions on Emergency Online Learning during the COVID-19 Pandemic: Challenges and Successes

Declaration of the COVID-19 pandemic by the World Health Organization in 2020 forced many schools to switch to emergency virtual instruction. This situation provided an opportunity to explore the effectiveness of online learning from students’ perspectives. To discover best practices for online learning, 584 STEM students at California State Polytechnic University Pomona (Cal Poly Pomona) were surveyed about their Spring and Fall 2020 experiences. Some of the obstacles students faced were adapting to a new lifestyle, feeling disconnected, managing schedule and workload, and overcoming distractions. Despite difficulties, 61% of students benefited from the flexibility, convenience, and increased productivity. The time students normally spent commuting to and parking on campus was instead spent getting more sleep, studying, working extra hours, spending time with family, and practicing self-care. Another major benefit was the increased accessibility to course materials posted online. Major themes from students’ responses were belonging, organization and transparency, and the need for real-world applications. Incorporating these strategies enhance the effectiveness of teaching methods. Responses along with some problem-solving suggestions that can improve the effectiveness of both online and in-person learning are discussed.

The computational benefits of episodic memory in spatial learning

Episodic memory has been studied extensively in the past few decades, but so far little is understood about how it is used to affect behavior. Here we postulate three learning paradigms: one-shot learning, replay learning, and online learning, where in the first two paradigms episodic memory is retrieved for decision-making or replayed to the neocortex for extracting semantic knowledge, respectively. In the third paradigm, the neocortex directly extracts information from online experiences as they occur, but does not have access to these experiences afterwards. By using visually-driven reinforcement learning in simulations, we found that whether an agent is able to solve a task by relying on the three learning paradigms depends differently on the number of learning trials and the complexity of the task. Episodic memory can, but does not always, have a major benefit for spatial learning, and its effect differs for the two modes of accessing episodic information. One-shot learning is initially faster than replay learning, but the latter reaches a better asymptotic performance. We believe that understanding how episodic memory drives behavior will be an important step towards elucidating the nature of episodic memory.

Local Delivery and Glioblastoma: Why Not Combining Sustained Release and Targeting?

Glioblastoma is one of the most aggressive brain tumors and is associated with a very low overall median survival despite the current treatment. The standard of care used in clinic is the Stupp's protocol which consists of a maximal resection of the tumor when possible, followed by radio and chemotherapy using temozolomide. However, in most cases, glioblastoma cells infiltrate healthy tissues and lead to fatal recurrences. There are a lot of hurdles to overcome in the development of new therapeutic strategies such as tumor heterogeneity, cell infiltration, alkylating agent resistance, physiological barriers, etc., and few treatments are on the market today. One of them is particularly appealing because it is a local therapy, which does not bring additional invasiveness since tumor resection is included in the gold standard treatment. They are implants: the Gliadel® wafers, which are deposited post-surgery. Nevertheless, in addition to presenting important undesirable effects, it does not bring any major benefit in the therapy despite the strategy being particularly attractive. The purpose of this review is to provide an overview of recent advances in the development of innovative therapeutic strategies for glioblastoma using an implant-type approach. The combination of this local strategy with effective targeting of the tumor microenvironment as a whole, also developed in this review, may be of interest to alleviate some of the obstacles encountered in the treatment of glioblastoma.

Parasitoids of the uniquely social colletid bee Amphylaeus morosus (Hymenoptera: Colletidae) in Victoria

Understanding how nest parasites contribute to brood mortality rates in host species is an important step towards uncovering the potential implications for host behaviour. This can be especially important for understanding the evolution of social living, where defence against parasites is often posited as a major benefit of cooperative nesting. Only two parasitoid species have previously been reported for the only known social colletid bee, Amphylaeus morosus: the gasteruptiid wasp, Gasteruption primotarsale, and the mutillid, Ephutomorpha tyla. Here we report six additional parasitoid species of A. morosus: the gasteruptiid wasps G. atrinerve, G. globiceps, G. melanopoda and G. cinerescens; the bombyliid fly Anthrax maculatus; and the mutillid wasp Ephutomorpha aff. varipes. The mechanisms of parasitism for these eight parasitoid species are described in combination with how they operate throughout the host brood rearing period and whether benefits of social nesting vary across the season.

Passive system of cooling and refrigerant fluid circulation, assisted by capillary pumping

We propose a concept design of a cooling system, primarily targeting gas-insulated switchgear enclosures which use a mixture of a refrigerant fluid, such as Novec™ 649, and a non-condensable gas for electrical insulation. The novel open-loop system relies on evaporative cooling assisted by capillary pumping, and refrigerant vapor condensation on the walls of the system enclosure. The results of experiments on a laboratory prototype are presented and discussed. Besides cooling, a major benefit of the system is in facilitating the circulation of the gas mixture in the enclosure.

Exploring Secondary Metabolites in Coffee and Tea Food Wastes

Coffee and tea are popular beverages worldwide, and therefore generate large amounts of waste. Here we describe the caffeine content and phenolic profile in three types of teas and coffees, and how they vary with two successive extractions. Although the first extraction was far more efficient than the second, green tea also showed a high content of flavanols in the second extraction, as did mate tea for phenolic acids. Black tea could also be a good option since caffeine content was highest in both extractions. Water also proved to be the most effective solvent in almost all cases, which represent a major benefit for urban horticulture, as it is a simple extraction method from an easily accessible source. Coffee and tea residues are a rich source of caffeine and phenolic compounds that could potentially be used as alternatives to conventional pesticides.

Sustainability of the Dairy Industry: Emissions and Mitigation Opportunities

Dairy cattle provide a major benefit to the world through upcycling human inedible feedstuffs into milk and associated dairy products. However, as beneficial as this process has become, it is not without potential negatives. Dairy cattle are a source of greenhouse gases through enteric and waste fermentation as well as excreting nitrogen emissions through their feces and urine. However, these negative impacts vary widely due to how and what these animals are fed. In addition, there are many promising opportunities for further reducing emissions through feed and waste additives. The present review aims to further expand on where the industry is today and the potential avenues for improvement. This area of research is still not complete and additional information is required to further improve our dairy systems impact on sustainable animal products.

The Economic Results of Investing in Precision Agriculture in Durum Wheat Production: A Case Study in Central Italy

Today, precision agriculture technologies (PATs) can be considered a tool for the management of the farm which allows the agricultural entrepreneur to optimise inputs, reduce costs, and offer the best quantitative and qualitative agricultural products. In Italy, the number of digital farmers is still low; therefore, it is not yet possible to assess with certainty the actual economic benefits that technologies bring to the farm. To bridge this gap, the paper proposes, through the analysis of a case study, an assessment of the economic efficiency of an Italian cereal farm that has invested in precision agriculture. The results reveal that, unlike what is reported in the literature, after the technological adoption, the farm keeps both the yield and variable costs stable. However, the major benefit is recorded in the decrease in labour costs (−20%) and in the reduction of pesticides (−53%). The increase in the quantity of nitrogen (+11%) and of seed distributed in the field (+5%) indicates that, in the face of a significant increase in total costs due to the capital invested in technology, the farm aims to intensify production rather than reduce agricultural inputs.

A Robust Lightweight Data Security Model for Cloud Data Access and Storage

In this paper, an efficient lightweight cloud-based data security model (LCDS) is proposed for building a secured cloud database with the assistance of intelligent rules, data storage, information collection, and security techniques. The major intention of this study is to introduce a new encryption algorithm to secure intellectual data, proposing a new data aggregation algorithm for effective data storage and improved security, developing an intelligent data merging algorithm for accessing encrypted and original datasets. The major benefit of the proposed model is that it is fast in the encryption process at the time of data storage and reduced decryption time during data retrieval. In this work, the authors proposed an enhanced version of the hybrid crypto algorithm (HCA) for cloud data access and storage. The proposed system provides secured storage for storing data within the cloud.

Exact solutions of Hirota–Maccari system forced by multiplicative noise in the Itô sense

In this article, the stochastic Hirota–Maccari system forced in the Itô sense by multiplicative noise is considered. We just use the He’s semi-inverse method, sine–cosine method, and Riccati–Bernoulli sub-ODE method to get new stochastic solutions which are hyperbolic, trigonometric, and rational. The major benefit of these three approaches is that they can be used to solve similar models. Furthermore, we plot 3D surfaces of analytical solutions obtained in this article by using MATLAB to illustrate the effect of multiplicative noise on the exact solution of the Hirota–Maccari method.