Potential for Further Evolution of CRDs

Although Grondona prepared detailed guidelines that his system should be implemented in relation to durable, essential, basic imported commodities, he also understood that, once established successfully, CRDs' operations could evolve in various ways in order to achieve greater benefits. For example, the inclusion of precious metals such as gold and silver, albeit on a somewhat different basis than industrial commodities, is an interesting possibility. Grondona also recommended that other products such as basic manufactured components like standardised steel or aluminium strip could be included. Another potential evolution is a CRD's role in a currency crisis: a sudden change in the exchange rate would be countered to some extent by a CRD being asked to sell or buy commodities, which would tend to resist the initial change. A CRD's terms of operation might be adjusted in order to strengthen its influence as such a countermeasure.

Comparative study between chaos theory and the innovation process

This article aims to make a correlation between chaos theory and the innovation process. Unlike what the name seems to be, chaos theory is linked to discovering simple patterns and laws that govern several complex phenomena. The complex is also innovation; therefore, this article makes an analogy of the innovation process from its conception to its implementation in the market with three principles of chaos theory: system, nonlinearity, and complexity. The innovative organization is characterized by constant innovation and complexity in nature. Uncertainty is an essential feature of innovation, which means an inability to predict the prospecting processor's outcome to set the best way to achieve a goal. Unpredictability is one of the characteristics of chaotic systems in which any minimal initial change in their conditions causes drastic changes in their course. Based on these premises, analogies were made to serve as a basis for further studies.

The Effect of Forward Testing as a Function of Test Occasions and Study Material

It has long been known that one of the most effective study techniques is to be tested on the to-be-remembered material, a phenomenon known as the testing effect. Recent research has also shown that testing of previous materials promotes the learning of new materials, a phenomenon known as the forward testing effect. In this paper, as of yet unexplored aspects of the forward testing effect related to face-name learning are examined; continuous and initial testing are compared to restudying, the effects of an initial test on subsequent learning, and whether an initial change of domain (change from one topic to another) regarding study material affects the robustness of the effect. An experiment (N = 94) was performed according to a 2 (Material: word pairs/face-name pairs in Block 1) × 3 (Test occasions: Blocks 1–4/Blocks 1 and 4/Block 4) complex between-groups design. The results showed that no difference between testing and repetition could be observed regarding the recall of faces and names. The restudy groups incorrectly recalled more names from previous lists in the last interim test compared to the tested groups, which supports the theory that interim tests reduce proactive interference. The results also suggest that the number of test occasions correlates with the number of incorrect recalls from previous lists. These results, in contrast to previous studies, highlight a potential uncertainty about the forward testing effect linked to the robustness of the phenomenon, the specificity in execution, and generalizability.

Evaluation of Electroencephalography, Behaviour and Eye Temperature in Response to Surgical Castration in Sheep

Castration has been demonstrated to cause pain in sheep. However, it is routinely performed for management purposes. Electroencephalography (EEG) has been used successfully to measure pain in lambs in response to castration and other husbandry procedures in livestock. The aim of this study was to evaluate the use of EEG as a measure of pain and analgesia in conscious lambs undergoing castration on farm over a 24 h period. EEG responses were compared to behavioural observations and changes in ocular temperature via infrared thermography. Twenty-four merino ram lambs (18.63 ± 2.06 kg) were used in this study. Lambs were randomly allocated to one of the following treatment groups: (1) castration with pre-surgical administration of meloxicam (0.5 mg/kg Metacam 20 mg/mL injected subcutaneously into the skin of the neck 15 min prior to recording) and lignocaine (applied via intra-testicular injection five minutes prior to castration, 2 mL lignocaine hydrochloride 20 mg/mL, Troy/Ilium) (CML, n = 8); (2) castration only (C, n = 8); (3) sham castration, handling only (SC, n = 8). EEG was recorded for 5 mins pre-procedure (prior to any intervention), and for 5 mins post-procedure at 0, 1, 4 and 24 h. Behavioural reactions to the procedure were scored, and behaviours were scan sampled at 5 min intervals at the above time points, by blinded observers. Eye temperature was measured for five-minute intervals at each time point. EEG decreased from baseline to 0 h for CML and C groups (p < 0.001), C group values returned similar to baseline at 24 h. Eye temperature increased post-castration at 0 h for C group, no initial change was seen for CML or SC groups. CML and C groups were more likely to have higher reaction scores and showed more abnormal behaviours (p = 0.017). CML and C groups had similar results, indicating minimal effect of analgesic intervention. Lambs in SC group showed significant EEG changes, suggesting that stress from handling also impacted EEG results.

Employing machine learning techniques to assess requirement change volatility

Lack of planning when changing requirements to reflect stakeholders’ expectations can lead to propagated changes that can cause project failures. Existing tools cannot provide the formal reasoning required to manage requirement change and minimize unanticipated change propagation. This research explores machine learning techniques to predict requirement change volatility (RCV) using complex network metrics based on the premise that requirement networks can be utilized to study change propagation. Three research questions (RQs) are addressed: (1) Can RCV be measured through four classes namely, multiplier, absorber, transmitter, and robust, during every instance of change? (2) Can complex network metrics be explored and computed for each requirement during every instance of change? (3) Can machine learning techniques, specifically, multilabel learning (MLL) methods be employed to predict RCV using complex network metrics? RCV in this paper quantifies volatility for change propagation, that is, how requirements behave in response to the initial change. A multiplier is a requirement that is changed by an initial change and propagates change to other requirements. An absorber is a requirement that is changed by an initial change, but does not propagate change to other requirements. A transmitter is a requirement that is not changed by an initial change, but propagates change to other requirements. A robust requirement is a requirement that is not changed by an initial change and does not propagate change to other requirements. RCV is determined using industrial data and requirement network relationships obtained from previously developed Refined Automated Requirement Change Propagation Prediction (R-ARCPP) tool. Useful complex network metrics in highest performing machine learning models are discussed along with the limitations and future directions of this research.

The Circadian-clock Regulates the Arabidopsis Gravitropic Response

For long-term space missions, it is necessary to understand how organisms respond to changes in gravity. Plant roots are positively gravitropic; the primary root grows parallel to gravity's pull even after being turned away from the direction of gravity. We examined if this gravitropic response varies depending on the time of day reorientation occurs. When plants were reoriented in relation to the gravity vector or placed in simulated microgravity, the magnitude of the root gravitropic response varied depending on the time of day the initial change in gravity occurred. The response was greatest when plants were reoriented at dusk, just before a period of rapid growth, and were minimal just before dawn as the plants entered a period of reduced root growth. We found that this variation in the magnitude of the gravitropic response persisted in constant light (CL) suggesting the variation is circadian-regulated. Gravitropic responses were disrupted in plants with disrupted circadian clocks, including plants overexpressing Circadian-clock Associated 1 (CCA1) and elf3-2, in the reorientation assay and on a 2D clinostat. These findings indicate that circadian-regulated pathways modulate the gravitropic responses, thus, highlighting the importance of considering and recording the time of day gravitropic experiments are performed.

Form und Funktion

This essay deals with the terms “form” and “function” as well as their relationship insofar as they are still used in philosophical and design-theory discourse to determine the aesthetic dimension of designed artefacts, especially of everyday objects, and often also to distinguish them from objects of art. I discuss whether our common understanding of these terms and their relationship is an appropriate instrument for such determinations. What is up for discussion here are not only conceptions of functional beauty with regard to design methodology and the philosophical discourse on aesthetics, in which form and function become thematic, but also basic concepts of philosophical aesthetics itself. It is shown that the philosophical understanding of design aesthetics and the concepts of form and function are determined by profound preliminary decisions that restrict our access to the aesthetic dimension of designed artefacts, and a conceptualisation of an initial change in thinking is proposed.

Detection of Road Surface Changes from Multi-Temporal Unmanned Aerial Vehicle Images Using a Convolutional Siamese Network

Road quality commonly decreases due to aging and deterioration of road surfaces. As the number of roads that need to be surveyed increases, general maintenance—particularly surveillance—can be quite costly if carried out using traditional methods. Therefore, using unmanned aerial vehicles (UAVs) and deep learning to detect changes via surveys is a promising strategy. This study proposes a method for detecting changes on road surfaces using pairs of UAV images captured at different times. First, a convolutional Siamese network is introduced to extract the features of an image pair and a Euclidean distance function is applied to calculate the distance between two features. Then, a contrastive loss function is used to enlarge the distance between changed feature pairs and reduce the distance between unchanged feature pairs. Finally, the initial change map is improved based on the preliminary differences between the two input images. Our experimental results confirm the effectiveness of this approach.