Distinct Strategies Research Articles

English communication strategies among Thai logistics professionals

Proficiency in English as a foreign language (EFL) holds significant importance in various professional domains, including the field of logistics. However, not all individuals proficient in EFL possess effective English communication skills. Hence, the objective of this research is to investigate the English communication strategies employed by Thai professionals working in the logistics industry. To receive participants' perspectives, a comprehensive approach involving questionnaires and focus-group interviews was employed. The survey results revealed that the most frequently utilized strategies encompassed rechecking, revising, identifying key concepts, and employing dictionaries, while the least commonly used strategies were self-development, guessing, word coinage, and the use of all-purpose words, respectively. Furthermore, the in-depth insights derived from the interviews revealed that participants employed English communication for diverse purposes, including email correspondence, meetings, ordering processes, and purchasing, thereby necessitating the utilization of distinct strategies for each of the four language skills. Remarkably, website translation tools emerged as vital resources for Thai logistics professionals, forming an integral component of their digital strategy. Ultimately, the findings of this study bear relevance to both pedagogical practices and business English training programs, with specific implications for the logistics industry.

Emotion malleability beliefs matter in emotion regulation: a comprehensive review and meta-analysis

ABSTRACT Individuals’ beliefs about the malleability of emotions have been theorised to play a role in their psychological distress by influencing emotion regulation processes, such as the use of emotion regulation strategies. We conducted a meta-analysis to test this idea across studies with a focus on the relationships between emotion malleability beliefs and five distinct emotion regulation strategies: cognitive reappraisal, suppression, avoidance, rumination, and acceptance. Further, using two-stage meta-analytic structural equation modelling (TSSEM), we examined whether the emotion regulation strategies mediate the cross-sectional relationship between emotion malleability beliefs and psychological distress across studies. Thirty-seven studies were included in the meta-analyses and 55 cross-sectional studies were included in the TSSEM. Results demonstrated that, across studies, emotion malleability beliefs were significantly associated with greater use of putatively helpful strategies (particularly with cognitive reappraisal) and less use of putatively unhelpful strategies (particularly with avoidance). The use of cognitive reappraisal and avoidance partially mediated the relationship between emotion malleability beliefs and psychological distress. These results highlight the importance of considering beliefs about the malleability of emotions in the context of emotion regulation. These findings suggest the potential role of emotion malleability beliefs in interventions for individuals with emotion regulation-related difficulties and psychological distress.

Empowering Graph Neural Networks with Block-Based Dual Adaptive Deep Adjustment for Drug Resistance-Related NcRNA Discovery.

Drug resistance to chemotherapeutic agents remains a formidable challenge in cancer treatment, significantly impacting treatment efficacy. Extensive research has exposed the intimate involvement of noncoding RNAs (ncRNAs) in conferring resistance to cancer drugs. Understanding the intricate associations between ncRNAs and drug resistance is of pivotal importance in advancing clinical interventions and expediting drug development. However, traditional biological experimental methods are hampered by limitations, such as labor intensiveness, time consumption, and constraints in scalability. Addressing these challenges necessitates the development of efficient computational methods for the accurate prediction of potential ncRNA-drug resistance associations (NDRA). However, most existing predictive models primarily focus on known ncRNA-drug resistance associations, often neglecting the critical aspect of similarity information between ncRNAs and drug resistance. This oversight may hinder the accuracy of characterizing these associations. To overcome the limitations of existing computational models, we proposed B-NDRA, a computational framework designed for the discovery of drug resistance-related ncRNA. Initially, we constructed a heterogeneous graph that integrates ncRNA-drug resistance pairs, leveraging both known associations and similarity fusion information between ncRNAs and drug resistance. Subsequently, we employed an attention mechanism to aggregate local features of graph nodes following a dimensionality reduction of node features. Further, a graph neural network (GNN) facilitated the learning of global node embeddings. Notably, the integration of dual adaptive deep adjustment architectures, encompassing intrablock and interblock methodologies, enabled efficient extraction of global features while balancing local and global features. Finally, B-NDRA employed a multilayer perceptron to predict associations between ncRNAs and drug resistance. Through rigorous 5-fold cross-validation, B-NDRA achieved average AUC, AUPR, Accuracy, Precision, Recall, and F1-score values of 92.2%, 91.9%, 84.88%, 86.9%, 82.37%, and 84.44%, respectively. Furthermore, comparative evaluations were conducted on established models, namely, GAEMDA, GRPAMDA, and LRGCPND. The results, obtained through three distinct 5-fold cross-validation strategies, demonstrated a notable performance improvement across almost all metrics for our B-NDRA. Specific case studies targeting Doxorubicin and Imatinib further validated the practicality of our B-NDRA in discovering potential NDRA. These results confirm the potential of our B-NDRA as a valuable tool in advancing cancer research and therapeutic development. The source code and data set of B-NDRA can be found at https://github.com/XuanLi1145/B-NDRA.

Granting leaders priority exit options promotes and jeopardizes cooperation in social dilemmas

Cooperation in human interactions often involves voluntary participation rather than obligatory requirements. This paper investigates the impact of voluntary exit strategies on cooperation within scale-free networks, employing spatial prisoner’s dilemma games as the framework. We analyze three distinct exit strategies: leader priority exit, fringe priority exit, and random exit, across varying dilemma strengths. Our findings reveal that in scenarios of weak dilemma strength, leader priority exit significantly diminishes cooperation, particularly when only a small subset of players is granted exit rights. In contrast, under strong dilemma strength conditions, both leader priority and random exits serve to unexpectedly bolster cooperation. This enhancement is primarily facilitated by high-degree nodes opting out, thereby enabling low-degree nodes to establish stable cooperation. Furthermore, our study underscores the pivotal role of network structure. In assortative networks, random exit emerges as the most effective strategy in promoting cooperation. On the other hand, disassortative networks exhibit a substantial increase in cooperation levels when employing leader priority exit. These results highlight the intricate interplay between exit strategies, dilemma strength, and network topology, offering critical insights for promoting cooperation in varied social contexts.

Robust Two-Dimensional InSAR Phase Unwrapping via FPA and GAU Dual Attention in ResDANet

Two-dimensional phase unwrapping (2-D PU) is vital for reconstructing Earth’s surface topography and displacement from interferometric synthetic aperture radar (InSAR) data. Conventional algorithms rely on the postulate, but this assumption is often insufficient due to abrupt topographic changes and severe noise. To address this challenge, our research proposes a novel approach utilizing deep convolutional neural networks inspired by the U-Net architecture to estimate phase gradient information. Our approach involves downsampling the input data to extract crucial features, followed by upsampling to restore spatial resolution. We incorporate two attention mechanisms—feature pyramid attention (FPA) and global attention upsample (GAU)—and a residual structure in the network’s structure. Thus, we construct ResDANet (residual and dual attention net). We rigorously train ResDANet utilizing simulated datasets and employ an L1-norm objective function to minimize the disparity between unwrapped phase gradients and those calculated by ResDANet, yielding the final 2-D PU results. The network is rigorously trained using two distinct training strategies and encompassing three types of simulated datasets. ResDANet exhibits excellent robust performance and efficiency on simulated data and real data, such as China’s Three Gorges and an Italian volcano.

Relatedness and regional economic complexity: Good news for some, bad news for others

PurposeThis article aims to evaluate the entry and exit of companies from local productive structures, with a specific focus on the sectoral complexity of these activities and the complexity of these portfolios. The study focuses on empirically demonstrating the thesis that related economic diversification exacerbates the development gap between more and less complex regions.Design/methodology/approachThe article uses indicators formulated by the economic complexity approach. They allow a relevant descriptive analysis of the economic diversification process in Brazilian micro-regions and provide the foundation for the econometric tests conducted. Through three distinct estimation strategies (OLS, logit, probit), the influence of complexity and relatedness on the entry and exit events of firms from local portfolios is tested.FindingsIn all estimated models, the stronger relationship between an activity and a portfolio significantly increases its probability of entering the productive structure and, at the same time, acts as a significant factor in preventing its exit. Furthermore, the results reveal that the complexity of a sector reduces the probability of its specialization in less complex regions while increasing it in more complex regions. On the other hand, sectoral complexity significantly increases the probability of a sector leaving less complex local structures but has no significant effect in highly complex regions.Research limitations/implicationsDue to the data used, the indicators are calculated considering only formal job numbers. Additionally, the tests do not detect the influence of spatial issues. These limitations should be addressed by future research.Practical implicationsThe article characterizes a prevailing process of uneven development among Brazilian regions and brings relevant implications, primarily for policymakers. Specifically, for less complex regions, policies should focus on creating opportunities to improve their diversification capabilities in complex sectors that are not too distant from their portfolios.Originality/valueThe article makes an original contribution by proposing an evaluation of regional diversification in Brazil with a focus on complexity, introducing a more detailed differentiation of regions based on their complexity levels and examining the impact of sectoral complexity on diversification patterns within each group.

Collision avoidance behaviours of luggage-laden pedestrians

Understanding how pedestrians move and avoid collisions is essential for ensuring safety in crowded environments. In this study, we conducted detailed experiments, focusing on variables such as movement type (walking and running), encounter angles (90 and 180 degrees), and pedestrian characteristics. We analyzed a total of 168 trajectories to gain insights into the collision avoidance strategies employed by luggage-laden pedestrians. Our study highlights the significant impact of luggage on the minimum avoidance distance in vertical encounters, while head-on approaches show limited influence. We use the minimum predicted distance to evaluate time avoidance time, with particular focus on the presence of luggage as a pivotal determinant. Furthermore, we conduct a comprehensive categorization on pedestrian behaviors when carrying luggage, taking into account their speed and trajectory. Our findings reveal distinct strategies adopted by luggage-laden pedestrians when avoiding from the left and right sides. Interestingly, pedestrians employ more efficient conflict mitigation tactics when interacting with luggage-laden individuals approaching from the left compared to the right side. These insights are valuable for optimizing crowd management strategies, particularly in transportation hubs with luggage-laden crowds.

Exploring parental risk perceptions in family travel in rural destinations

ABSTRACT Family travel can offer an enjoyable and enriching experience for both parents and children, but it also comes with potential risks. Children's vulnerability is a significant concern during family trips, making it imperative for parents to address these risks. Drawing upon protection motivation theory and employing a mixed-method approach, the study aimed to examine parental perceptions of risks associated with children during trips to rural areas, utilizing the phenomenological method alongside exploratory and confirmatory factor analysis. The findings reveal that parents perceive five types of risks pertaining to their children during vacations, including risks related to safety and health, environmental and natural hazards, social interactions, child safety and security and emotional and psychological well-being. Furthermore, two distinct mechanisms that parents use to manage risks were also identified: prevention and readiness/reaction mechanisms. Overall, this study contributes to the emerging field of child-centric tourism research by illuminating parents’ distinctive risk perception and management strategies during family vacations, offering insights for the development of tourism policies and practices that prioritize the safety and well-being of children and families.

Sampling business process event logs with guarantees

SummaryEvent log sampling has emerged as a key research focus in the field of process mining, aiming to enhance the efficiency of various process mining tasks, including model discovery, conformance checking, and process prediction. However, current log sampling techniques often fail to ensure high‐quality sample logs. This paper introduces a novel framework to support efficient event log sampling without compromising the quality of the sample log compared to the original one. The approach revolves around the consideration of directly‐follows relation (DFR) among business tasks as the fundamental behavior unit of an event log. By ensuring the DFR equivalence between the original and sample logs, the proposed technique addresses the challenge of sample log quality from the model discovery point of view. The framework is instantiated by seven distinct sampling strategies each has its own specialty and is fully implemented in the open‐source process mining tool platform ProM. To validate its effectiveness, we conducted a comprehensive experimental evaluation using 12 publicly available real‐life event logs against state‐of‐the‐art sampling techniques. The results clearly demonstrate that our technique significantly improves model discovery efficiency while upholding high quality of the discovered models.

Unlocking the power of motor imagery: a comprehensive review on its application in alleviating foot pain.

Motor imagery is a cognitive process that involves mentally simulating movements without physical execution. It has been studied in the context of foot pain to understand the role of motor cortical reorganization and its impact on motor imagery abilities. However, further research is needed to establish consistent evidence regarding the relationship between motor imagery and foot pain. This review analyzed five relevant articles that investigated motor imagery in the context of foot pain. The studies involved participants with various conditions, including leg amputation, chronic leg pain, complex regional pain syndrome, and Achilles tendinopathy. Different methodologies were employed, including motor cortical mapping, foot laterality recognition tasks, EEG recordings, and treatment interventions incorporating motor imagery. The findings indicated that individuals with leg amputation exhibited functional reorganization in upper limb motor cortical maps, with a breakdown in the inhibitory relationship between foot and hand representations. Participants with chronic leg pain demonstrated slower and less accurate performance on foot laterality recognition tasks compared to healthy controls. Complex regional pain syndrome patients displayed distinct motor imagery strategies and responded differently to first-person and third-person perspectives. EEG studies revealed differences in brain activity during motor imagery tasks under pain-free and pain conditions. Treatment interventions incorporating motor imagery showed promising outcomes in improving functional outcomes and reducing pain levels. Motor imagery plays a significant role in foot pain conditions, although the evidence is still emerging. The findings suggest that motor imagery abilities may be affected by leg amputation, chronic pain, and complex regional pain syndrome. Further research is needed to establish standardized protocols for assessing motor imagery, identify specific patient populations that may benefit most from motor imagery interventions, and explore long-term effects. Integrating motor imagery into clinical practice has the potential to enhance rehabilitation approaches and improve outcomes in foot pain management.

Control strategy under pressure situations: performance pressure conditionally enhances proactive control.

Previous research and theories have demonstrated that attentional control plays a crucial role in explaining the choking phenomenon (i.e., the performance decrements) under pressure situations. Attentional control is thought to function through two distinct control strategies: proactive control (i.e., a sustained and anticipatory strategy of control) and reactive control (i.e., a transient strategy of control). However, little is known about how performance pressure affects these control strategies. The present study was designed to address this issue. Participants were instructed to complete a continuous performance task (AX-CPT40) under pressure situations. The results showed that individuals under high-pressure situations tended to use proactive control rather than reactive control. Moreover, performance pressure resulted in a more liberal response bias following an A-cue, consistent with an increased use of proactive control. Importantly, the proactive behavioral index calculated on RTs showed that the increased proactive control mainly occurred in the short interval between the cue and probe, but not in the long interval. This suggests that individuals under high-pressure situations are unlikely to employ a proactive control strategy in a situation that requires more attentional resources. In summary, our results provide initial evidence that performance pressure conditionally enhances proactive control, which contributes to a deeper understanding of the dynamic adjustment of control strategies under pressure situations.

Humans in XAI: increased reliance in decision-making under uncertainty by using explanation strategies

IntroductionAlthough decision support systems (DSS) that rely on artificial intelligence (AI) increasingly provide explanations to computer and data scientists about opaque features of the decision process, especially when it involves uncertainty, there is still only limited attention to making the process transparent to end users.MethodsThis paper compares four distinct explanation strategies employed by a DSS, represented by the social agent Floka, designed to assist end users in making decisions under uncertainty. Using an economic experiment with 742 participants who make lottery choices according to the Holt and Laury paradigm, we contrast two explanation strategies offering accurate information (transparent vs. guided) with two strategies prioritizing human-centered explanations (emotional vs. authoritarian) and a baseline (no explanation).Results and discussionOur findings indicate that a guided explanation strategy results in higher user reliance than a transparent strategy. Furthermore, our results suggest that user reliance is contingent on the chosen explanation strategy, and, in some instances, the absence of an explanation can also lead to increased user reliance.

Perioperative knowledge, self-efficacy and clinical practices related to postoperative delirium care in older people across geographical regions in Australia.

In older people undergoing surgery, there is a lower tolerance for complications. This highlights the need for documented clinical practices on proper prevention, pre-screening and management of complications such as postoperative delirium (POD). Evidence-based clinical practice guidelines exist for delirium management; however, the management of delirium in clinical settings differs widely. This study aims to develop an understanding of the knowledge, components of self-efficacy (confidence/competence), and clinical practice related to POD care among preoperative and recovery nurses across different types of geographical locations in Australia. A 27-item online survey was sent out to professional associations focused on perioperative care across Australia. The participants included practicing registered nurses specialising in perioperative care. The main outcome measures were Knowledge, confidence/competence and clinical practice relating to POD care in older patients. Respondents were categorised into two groups-major cities and rural and remote. Mean age was 46.3 years for respondents in Australian major cities and 49.5 for the comparison group. There was a statistically significant difference between Australian major cities and 'rural and remote' in confidence in detecting hyperactive delirium, 25.2% versus 11.7% respectively as well as managing hyperactive delirium, 13.8% versus 1.7%, respectively. Similar results were also observed on hypoactive delirium. Respondents from both groups did not mirror the ideal situation in managing a hyperactive delirium. Results from this study are inconclusive and there is no clear-cut observation in clinical practice or knowledge between the two geographical categories in this study. The absence of a distinct strategy with POD prevention highlights the need for a uniform approach and consensus on POD prevention and management in older people. This can be achieved by creating more awareness and education through professional development related to POD.

Manufacturer’s optimal distribution strategy in the platform supply chain: Bundling or add-on?

AbstractThis study delves into the realm of distribution strategies employed in retail markets, particularly focusing on the widely utilized bundle-and-add-on strategy. Three distinct distribution strategies are examined: bundled-by-the-base-manufacturer (BBBM), bundled-by-the-platform, and the add-on approach within a platform supply chain context. Through comprehensive analysis, this paper investigates the optimal distribution strategy for manufacturers. Significantly, our research reveals that in cases where only bundling is feasible, base manufacturers can reap benefits from a self-bundling strategy when both the platform’s commission rate and the marginal cost of the bundled product are low. Additionally, the platform stands to gain from this approach when the commission rate is low, and the marginal cost of the bundled product is either moderate or very high. Notably, win–win scenarios can emerge for both manufacturers and platforms through specific bundling or platform bundling strategies under specific conditions. Furthermore, the study demonstrates that the price of bundles under the add-on strategy will be lower, attracting more consumers, especially when the commission rate is low. Intriguingly, when the marginal cost of the base product is low, the add-on strategy consistently emerges as the optimal choice; as costs escalate, BBBM might be optimal, especially when the commission rate is low, showcasing a nuanced understanding of the interplay between costs and strategic choices in distribution. The major contribution of this paper lies in its nuanced analysis of these strategies and their implications for both manufacturers and platforms in the retail landscape.

The collapse of cooperation during range expansion of Pseudomonas aeruginosa.

Cooperation is commonly believed to be favourable in spatially structured environments, as these systems promote genetic relatedness that reduces the likelihood of exploitation by cheaters. Here we show that a Pseudomonas aeruginosa population that exhibited cooperative swarming was invaded by cheaters when subjected to experimental evolution through cycles of range expansion on solid media, but not in well-mixed liquid cultures. Our results suggest that cooperation is disfavoured in a more structured environment, which is the opposite of the prevailing view. We show that spatial expansion of the population prolongs cooperative swarming, which was vulnerable to cheating. Our findings reveal a mechanism by which spatial structures can suppress cooperation through modulation of the quantitative traits of cooperation, a process that leads to population divergence towards distinct colonization strategies.

Leftist governments, distributive strategies, and the politics of balance of payments-constrained growth in Chile and Uruguay

Leftist governments in peripheral economies have usually faced problems fulfilling their distributive mandate. Because of their inability to earn foreign exchange to pay for imports or service their debt, these governments often ended in epic balance of payments crises and renounced their electoral programs while embracing stabilization and fiscal austerity. This reflects the fact that economic growth and distribution in peripheral economies are fundamentally balance-of-payments-constrained. How can we understand variation in the form of distributive strategies advanced by left parties in government if they are subject to balance of payments constraints? And how can we understand the emergence of different growth models within those constraints? This article contributes to understanding the politics of peripheral growth models by studying the variation in left government distributive and growth strategies in the context of balance-of-payments (BoP) constrained growth. While the BoP constraints are real and challenging and the push for monetary and fiscal responsibility has been blunt, we argue that the extent to which governing left parties build linkages with grassroots and subordinated groups, and extant institutional architectures, allow spaces for political agency that leftist governments can use to pursue distinct distributive and growth strategies. In turn, these strategies allow for diverse ways of managing the macroeconomic implications of the BoP constraints.

Differential psychophysiological responses associated with decision-making in children from different socioeconomic backgrounds.

This study examined how socioeconomic status (SES) influences on decision-making processing. The roles of anticipatory/outcome-related cardiac activity and awareness of task contingencies were also assessed. One hundred twelve children (Mage = 5.83, SDage = 0.32; 52.7% female, 51.8% low-SES; data collected October-December 2018 and April-December 2019) performed the Children's Gambling Task, while heart rate activity was recorded. Awareness of gain/loss contingencies was assessed after completing the task. Distinct decision-making strategies emerged among low and middle/high-SES children. Despite similar awareness levels between SES groups, future-oriented decision-making was linked solely to the middle/high-SES group. Somatic markers did not manifest unequivocally. However, contrasting cardiac patterns were evident concerning feedback processing and the association between anticipatory activity and awareness (low: acceleration vs. middle/high: deceleration). Results are interpreted from an evolutionary-developmental perspective.

Contrasting Energy Storage Systems for Small-Scale Isolated Grids

The goal of this article is to examine the best electricity storage methods, both in terms of technology and economics, for tiny, independent electrical grids that are integrated with power plants that generate renewable energy sources (RES). As case studies, three autonomous Greek is-lands—Symi, Astypalaia, and Kastelorizo—with yearly and approximately for our work is peak demand of 3.9 MW, 2.1 MW, and 0.889 MW, respectively, are examined. Every island under inves-tigation has exceptional solar and wind potential, and their locations are perfect for the installation of seawater PHS (pumped hydro storage). Regarding the energy storage facilities, two distinct strat-egies are examined: PHS systems (for the two largest islands) and electrochemical storage, which is another name for lead acid or lithium-ion batteries. Potential RES units include photovoltaic instal-lations and wind farms. The analysed plants' dimensioning is optimised with a shared goal in mind: achieving an annual percentage of RES penetration above 69.9% while keeping the selling price of energy below the current specific production cost. The analysis is combined with the systems under examination's economic assessment. It is demonstrated that wind-PHS is still a competitive alterna-tive for Symi and Astypalaia given the proper land morphology for PHS installations, while a wind-p/v-batteries features as the best choice for Kastelorizo. Only with PHS support can 99.9% annual RES penetration be attained; with electrochemical batteries, annual RES penetration can range from 79.9 to 91.1%. Electricity selling prices between 199 and 349 €/kWh, which result in payback periods between five and nine years, ensure the economic viability.

A bizarre new plioplatecarpine mosasaurid from the Maastrichtian of Morocco

The Upper Maastrichtian of Morocco has produced a remarkably diverse fauna of mosasaurids, the most diverse known for any time or place. As apex predators, Mosasauridae provide a picture of the marine ecosystem just before the end-Cretaceous mass extinction. Here we describe a bizarre new plioplatecarpine mosasaurid, Khinjaria acuta, characterized by enlarged, dagger-like anterior teeth, short, robust jaws, and posterior elongation of the skull. Khinjaria is related to Goronyosaurus nigeriensis from Nigeria and Niger, and Gavialimimus almaghribensis from Morocco. These species form a distinct clade of specialized mosasaurids so far unknown outside of Africa. Mosasaurids show high endemism in the Maastrichtian, with different lineages occurring in different regions, implying that mosasaurid diversity is underestimated because of limited geographic sampling. The large size, robust jaws, akinetic skull, and bladelike teeth of Khinjaria suggest it was an apex predator, but the unusual skull and jaw differ from those of contemporary predators like Hainosaurus, Thalassotitan, and Mosasaurus, suggesting a distinct feeding strategy. Mosasaurids became increasingly specialized in the latest Cretaceous, repeatedly evolving to occupy the apex predator niche, suggesting a diverse marine ecosystem persisted up to the K/Pg boundary. Late Cretaceous marine ecosystems differ from modern marine ecosystems in the high diversity of large predators.

Restoring the discontinuous heat equation source using sparse boundary data and dynamic sensors

This study focuses on addressing the inverse source problem associated with the parabolic equation. We rely on sparse boundary flux data as our measurements, which are acquired from a restricted section of the boundary. While it has been established that utilizing sparse boundary flux data can enable source recovery, the presence of a limited number of observation sensors poses a challenge for accurately tracing the inverse quantity of interest. To overcome this limitation, we introduce a sampling algorithm grounded in Langevin dynamics that incorporates dynamic sensors to capture the flux information. Furthermore, we propose and discuss two distinct dynamic sensor migration strategies. Remarkably, our findings demonstrate that even with only two observation sensors at our disposal, it remains feasible to successfully reconstruct the high-dimensional unknown parameters.