Salient Points Research Articles

Conflict and Collaboration: Co-Teaching Dynamics in Bilingual Private Schools

This qualitative phenomenological inquiry aimed to explore the intricacies of conflict within the educational landscape, specifically focusing on its manifestation within the realm of private schools. The resultant dataset covered the perspectives of 37 teachers and four administrators through semi-structured interviews. This investigation leveraged the transformative power of in-depth interviews, engaging both co-teachers and school administrators, to unveil the layers of complexity hidden beneath the surface. The data analysis followed a series of thematic coding stages. The multifaceted findings uncovered a paradoxical landscape within the practice of co-teaching. While the practice itself was recognized as invaluable, a tapestry of conflicting issues emerged that warrant careful consideration. The salient points of discord that surfaced encompassed themes such avoidance of responsibility, inability to adapt to practice, not knowing the system, the partners paying more attention to their branch, intense program, lack of communication and different educational philosophies. Finally, a notable difference in perception arose: teachers often credited conflict resolution to the effectiveness of their administrators, whereas administrators leaned towards the idea that it depended on the competence of their fellow teachers. While teachers pushed for more in-service training and workshops, administrators argued that they already offer sufficient training opportunities. These results underscore the intricate dynamics within the education system. To facilitate successful co-teaching, both teachers and administrators must grasp this approach thoroughly.

Title, abstract, keywords, and authorship criteria

In the domain of medical research, the importance of developing precise and informative elements such as the title, abstract, keywords, and authorship criteria cannot be overstated. This article provides salient points on drafting these essential components for effective communication, dissemination, and ethical behavior in the medical research field.

Diabetes and Cervical Necrotizing Fasciitis: A Case Report

IntroductionCervical necrotizing fasciitis in diabetics is under-reported in the literature. We present a case report and discuss salient points in its management.

Totally minimally invasive laparoscopic robot-assisted Ivor Lewis esophagectomy: improved technique and outcomes over 200 cases.

Surgical resection of esophageal and gastroesophageal junction cancers is a very complex procedure with step learning curve. New technologies have made minimally invasive surgery possible, but challenges still remain for wide spread adoption of these techniques. This article aims to describe the outcomes and salient technical points of a totally minimally invasive, laparoscopic, robot-assisted Ivor Lewis esophagectomy (LRAMIE). Retrospective observational cohort study performed at a specialty cancer center using a prospectively maintained institutional database. Patients undergoing LRAMIE (laparoscopic abdomen, robotic chest) from 2014-2023 were included. Patients undergoing transhiatal and three-field esophagectomy were excluded. Operative and postoperative outcomes were compared over the study period to identify potential associations between outcomes over time. Two-hundred patients were identified who underwent LRAMIE. Median age was 65 years and most were male (87.5%). The open conversion rate was 1% (n=2), which occurred within the first 30 cases. Operative time and blood loss were improved at the 60-case mark (P<0.001). Anastomotic stricture rate improved after 50 cases, and leak rate improved after 80 cases. Postoperative length of stay improved at both 50 and 100 cases with a median LOS of 6 days after 100 cases. Rate of postoperative pneumonia, 30- and 90-day mortality were reduced after 100 cases, although not statistically significant for mortality due to too few events. Totally minimally invasive Ivor Lewis esophagectomy at a high-volume center is a safe procedure. Operative outcomes improved significantly after 50-80 cases, followed by improvement in anastomotic results and postoperative outcomes, with corresponding excellent oncologic outcomes.

The puzzling persistence of financial crises: A selective review of 2000 years of evidence

The high social costs of financial crises imply that economists, policymakers, businesses, and households have a tremendous incentive to understand, and try to prevent them. And yet, so far we have failed to learn how to avoid them. In this article, we take a novel approach to studying financial crises. We first build ten case studies of financial crises that stretch over two millennia, and then consider their salient points of differences and commonalities. We see this as the beginning of developing a useful taxonomy of crises – an understanding of the most important factors that reappear across the many examples, which also allows (as in any taxonomy) some examples to be more similar to each other than others. From the perspective of our review of the ten crises, we consider the question of why it has proven so difficult to learn from past crises to avoid future ones.

How do depressed people feel perceived by others? A qualitative study from the patient's perspective

BackgroundDepression is a condition which affects the individuals’ entire existence. The difficulty in emotionally tuning in to the environment and the resulting sensation of loneliness have frequently been described as salient points of depressive experience. To our knowledge there are no studies that phenomenologically analyze how depressed people feel perceived by others. MethodsAiming to fill this gap, this study builds on a previous work, which carried out a literature screening and a thematic analysis on the subjective experience of depression. The narratives collected in this way were analyzed using a bottom-up qualitative method (Consensual Quality Research). ResultsFour main categories of experience related to perceived social apprehension of depression have been identified: (1) feeling guilty about not meeting others' expectations, (2) feeling socially misunderstood, (3) bringing to light an inconvenient truth that others do not want to see, and (4) hiding from exposure to the judgement of others. LimitationsA broader and more specific data collection on the topic is missing. ConclusionsDepressed patients often feel misunderstood by their social environment, which belittles or dramatizes their condition or doesn't realize how otherwise normal requests can instead cost a lot of effort, generating feelings of guilt and inability. This frequently pushes depressed individuals to try to hide their condition by isolating themselves or wearing a social mask. The difficulty in emotional attunement already present in depression can only worsen in the face of this feeling of incomprehension, generating a vicious circle of non-recognition that ends up maintaining the depressive state.

Levenberg–Marquardt deep neural watermarking for 3D mesh using nearest centroid salient point learning

Watermarking is one of the crucial techniques in the domain of information security, preventing the exploitation of 3D Mesh models in the era of Internet. In 3D Mesh watermark embedding, moderately perturbing the vertices is commonly required to retain them in certain pre-arranged relationship with their neighboring vertices. This paper proposes a novel watermarking authentication method, called Nearest Centroid Discrete Gaussian and Levenberg–Marquardt (NCDG–LV), for distortion detection and recovery using salient point detection. In this method, the salient points are selected using the Nearest Centroid and Discrete Gaussian Geometric (NC–DGG) salient point detection model. Map segmentation is applied to the 3D Mesh model to segment into distinct sub regions according to the selected salient points. Finally, the watermark is embedded by employing the Multi-function Barycenter into each spatially selected and segmented region. In the extraction process, the embedded 3D Mesh image is extracted from each re-segmented region by means of Levenberg–Marquardt Deep Neural Network Watermark Extraction. In the authentication stage, watermark bits are extracted by analyzing the geometry via Levenberg–Marquardt back-propagation. Based on a performance evaluation, the proposed method exhibits high imperceptibility and tolerance against attacks, such as smoothing, cropping, translation, and rotation. The experimental results further demonstrate that the proposed method is superior in terms of salient point detection time, distortion rate, true positive rate, peak signal to noise ratio, bit error rate, and root mean square error compared to the state-of-the-art methods.

Point Cloud Registration Method Based on Geometric Constraint and Transformation Evaluation.

Existing point-to-point registration methods often suffer from inaccuracies caused by erroneous matches and noisy correspondences, leading to significant decreases in registration accuracy and efficiency. To address these challenges, this paper presents a new coarse registration method based on a geometric constraint and a matrix evaluation. Compared to traditional registration methods that require a minimum of three correspondences to complete the registration, the proposed method only requires two correspondences to generate a transformation matrix. Additionally, by using geometric constraints to select out high-quality correspondences and evaluating the matrix, we greatly increase the likelihood of finding the optimal result. In the proposed method, we first employ a combination of descriptors and keypoint detection techniques to generate initial correspondences. Next, we utilize the nearest neighbor similarity ratio (NNSR) to select high-quality correspondences. Subsequently, we evaluate the quality of these correspondences using rigidity constraints and salient points' distance constraints, favoring higher-scoring correspondences. For each selected correspondence pair, we compute the rotation and translation matrix based on their centroids and local reference frames. With the transformation matrices of the source and target point clouds known, we deduce the transformation matrix of the source point cloud in reverse. To identify the best-transformed point cloud, we propose an evaluation method based on the overlap ratio and inliers points. Through parameter experiments, we investigate the performance of the proposed method under various parameter settings. By conducting comparative experiments, we verified that the proposed method's geometric constraints, evaluation methods, and transformation matrix computation consistently outperformed other methods in terms of root mean square error (RMSE) values. Additionally, we validated that our chosen combination for generating initial correspondences outperforms other descriptor and keypoint detection combinations in terms of the registration result accuracy. Furthermore, we compared our method with several feature-matching registration methods, and the results demonstrate the superior accuracy of our approach. Ultimately, by testing the proposed method on various types of point cloud datasets, we convincingly established its effectiveness. Based on the evaluation and selection of correspondences and the registration result's quality, our proposed method offers a solution with fewer iterations and higher accuracy.

Active Data Selection and Information Seeking

Bayesian inference typically focuses upon two issues. The first is estimating the parameters of some model from data, and the second is quantifying the evidence for alternative hypotheses—formulated as alternative models. This paper focuses upon a third issue. Our interest is in the selection of data—either through sampling subsets of data from a large dataset or through optimising experimental design—based upon the models we have of how those data are generated. Optimising data-selection ensures we can achieve good inference with fewer data, saving on computational and experimental costs. This paper aims to unpack the principles of active sampling of data by drawing from neurobiological research on animal exploration and from the theory of optimal experimental design. We offer an overview of the salient points from these fields and illustrate their application in simple toy examples, ranging from function approximation with basis sets to inference about processes that evolve over time. Finally, we consider how this approach to data selection could be applied to the design of (Bayes-adaptive) clinical trials.

Enhancing young children's executive function through physical activities: A three-level meta-analysis

Background and aimsThe cultivation of executive functions during early childhood establishes the fundamental underpinning for advanced cognitive development in adulthood. Presently, there remains a degree of controversy surrounding the empirical findings pertaining to the impact of physical activity on the executive functions of young children. This study aimed at elucidating the potential moderating factors influencing the relationship between physical activity and the executive functions of young children. MethodsIn this study, we have conducted a meta-analysis encompassing twenty-four experimental studies with a total of 1426 participants. The meta-analysis was conducted using Stata version 17.0 and R version 4.3.2 software, with Hedges'g serving as the chosen effect size metric. ResultsThe outcomes of our investigation underscore the salient point that physical activity possesses the capacity to enhance the executive functions of young children. Furthermore, it is noteworthy that the developmental impact of physical activity on the executive functions of young children is notably contingent upon the duration of the intervention. The implementation of game-based pedagogical approaches within the realm of sports activities emerges as a particularly potent means to foster the development of executive functions in young children. ConclusionsThis meta-analysis serves to proffer quantitative evidence affirming the positive influence of physical activities on the executive functions of young children during their early developmental years. It carries substantial implications for practical application.

A LOOK INTO THE HISTORY OF STUDYING THE CONCEPTS OF "JOY" AND "SADNESS"

This article delves into the significance of studying emotions, specifically the concepts of "joy" and "sadness" within the realm of conceptual research, from the perspective of linguists. The paper reflects on the salient points of scientific approaches by German, Polish, and Iranian scholars on this topic. The contributions of Russian and Uzbek linguists to this field are also discussed in the context of conceptual studies.

Extending the boundaries of academic entrepreneurship: demonstrating the instrumentality of new logics for addressing social inclusion

Academic entrepreneurship (AE) has gained widespread acclaim globally for bridging the gap between academia and industry, as well as addressing the issue of socio-economic inclusion and the impact of research. In this paper, we both relate to, and extend scholarly work in the domains of AE and social inclusion by investigating the process of AE from an interplay of institutional logics perspective, with an emphasis on the diversity strands to which academic entrepreneurs belong. Drawing on extensive fieldwork of 55 academic researchers affiliated with 6 UK universities, our findings highlight the co-existence of profession, science logic and market logic, combined with the new logics that we introduce—diversity and impact logics—in capturing value through AE for addressing social inclusion. We make an empirical and theoretical contribution by demonstrating the instrumentality of these logics as enablers and constraints on the process of AE. We also advance the theory on logics and AE by demonstrating how the intersection of logics creates different kinds of salient points and pressures for academics and university policy makers. Closely linked to this, we make policy contributions by putting forward policy insights that are bottom-up and tailored based on a thorough understanding of the experiences and views of academic entrepreneurs.

Formation and evolution of vortex breakdown consequent to post design flow increase in a Francis turbine

Draft tube flow instability encountered under off-design operating conditions in hydraulic turbines significantly limits their operational flexibility. The instability arises consequent to a higher than threshold swirl content in the runner outflow and leads to vortex breakdown phenomenon in the draft tube cone. At high load condition, the phenomenon presents as an enlarged vortex core counter-rotating with respect to the runner. The flow situation is known to compromise the turbine efficiency besides the generation of unwanted effects such as power swings and large-scale pressure fluctuations. The present paper is the first to encapsulate a thorough numerical investigation on the formation and evolution of the enlarged vortex core alongside the consequent effects. A transient operating sequence between best efficiency and high load operating points in a model Francis turbine is simulated. Turbulence closure has been attained using the shear stress transport-scale adaptive simulations turbulence model. Dynamic meshing based on a Laplacian smoothing scheme has been used to account for mesh deformation arising from guide vane motion during load change. The pressure and velocity fields have been determined and analyzed to elucidate the physics of vortex breakdown, the phenomenon underlying the formation of the enlarged vortex core. Furthermore, pressure fluctuations at salient points in the domain have been analyzed using Fourier and short-time Fourier transforms. Finally, the enlarged vortex core formed in the draft tube has been visualized through the λ2 criterion. The core takes the shape of a cork-screw like compactly wound spiral structure extending up to the draft tube elbow.

Adapting an Environmental Scan for 'Insights Reporting': Learnings from an Online Brain Cancer Peer Support Platform.

When developing a digital health solution, product owners, healthcare professionals, researchers, IT teams, and consumers require timely, accurate contextual information to inform solution development. Insights Reporting can rapidly draw together information from literature, end users and existing technology to inform the development process. This was the case when creating an online brain cancer peer support platform where solution development was conducted in parallel with contextual information synthesis. This paper discusses the novel adaptation of an environmental scan methodology using codesign and multiple layers of qualitative rigor, to create Insights Reporting. This seven-step process can be completed in two months and results in salient points of knowledge that can rapidly inform the design of a solution, creating a shared understanding of a digital health phenomenon. Project members noted that Insights Reporting surfaces previously inaccessible knowledge, catalyzes decision-making and allows all stakeholders to influence the report agenda, affirming principles of digital health equity.

PE-Transformer: Path enhanced transformer for improving underwater object detection

Traditional object detection methods cannot effectively identify underwater objects with complex backgrounds, and it is difficult to fully obtain the details of small-scale underwater targets, resulting in poor detection performance. We propose a path-augmented Transformer detection framework to address these limitations to explore the semantic details of small-scale underwater targets in complex environments. On one hand, an embedded local path detection information scheme is devised to facilitate the interaction between high-level and low-level features, thereby enhancing the semantic representation of distinctive features of small-scale underwater targets. Rich dependency relationships are established between the acquired high-level and low-level features within the CSWin-Transformer framework, thus fortifying the semantic representation during the encoding phase. Furthermore, a individualized loss function is employed to optimize and fine-tune features at various hierarchical levels. On the other hand, a detection module with flexible and adaptive point representation that is different from conventional square detection methods is designed. This module covers the underwater target from any direction, and the salient point samples in classification localization and feature selection between points realize the feature selection improves the detection accuracy of underwater objects simultaneously. We designed a new weighted loss function to encourage the network to converge better. Experimental results on open-source underwater and remote sensing images of UTDAC, RUOD, and ADios show that the proposed method outperforms other underwater object detection methods in terms of precision(P), recall(R), comprehensive evaluation index of F1-score and FPS(frames per second).

A Policy Brief on the Application of Artificial Insemination (AI) in Lanao del Sur

This paper is a policy brief regarding the application of the Artificial Insemination (AI) as a mode of reproductive technology in Lanao del Sur. Lanao del Sur is one of the provinces in the Bangsamoro Autonomous Region in Muslim Mindanao (BARMM), the dwelling of Meranao tribes who practice Islam as their religion. The province has favorable climatic conditions for agricultural productions both in crops and animals’ production. Lanao del Sur is a landlocked province and is agriculturally rich having a well-suiting climate and extensively arable land, and it has a growing inventory of livestock such as carabao, cattle, goat, duck and chicken. This policy brief was based on the research findings conducted regarding the perceptions and acceptance of the AI among livestock growers. The goal of this policy brief is to provide baseline data about the farmers perceptions on AI and formulate a research-based policy recommendation to help the LGU, implementing agencies and the farmers for effective and efficient implementation of AI program in the province. The6 following are the salient points: 1) Meranao livestock growers’ perceptions about using Artificial Insemination (AI). 2) Capacitating the farmers towards adoption of artificial insemination, and breeding programs. 3) Ensuring efficiency of livestock production among farmers. 4) Increase the livestock production in Lanao del Sur.

The Moderating Effect of Youth Sports Coaches’ Identities on Their Professional Knowledge Orientations: A Social Network Perspective

The present study used social network analysis to investigate the moderating effect of individuals’ identities on the relationship between the structure of their networks and professional knowledge orientations (exploitation, exploration, and ambidexterity). It examined the ego networks of Israeli youth soccer coaches (N = 88) based on their vocational and personal sources of knowledge, including sportive, personal development, and friendship networks. The findings indicate that youth soccer coaches’ sportive identity embeddedness enhances their knowledge exploitation even when they have access to nonredundant, new, and versatile knowledge. But in the case of lower levels of athlete identity and nonredundant versatile networks, youth soccer coaches display ambidexterity by exploring novelty while relying on their familiar past knowledge and routines. Overall, the study describes the challenges of a single salient identity and points out the importance of cultivating diverse networks and multiple identities to facilitate effective and sustainable knowledge acquisition and utilisation for long-term professional advancement.

Energy Inventory in the Sliding and Rolling Regimes

The energy inventory during the transition between the sliding and rolling regimes has frequently received short shrift in the physics and engineering literature. Here we delve into the intricacies of sliding and rolling motion and emphasize the salient points often missed in a cursory analysis which tends to focus mostly on torque and angular momentum. We show, for instance, that when a spinning cylinder slides and skids and ends up rolling, it loses two-thirds of its initial kinetic energy doing work against friction. Moreover, this number is independent of the coefficient of friction! This sounds counterintuitive at first blush. We help the reader develop an intuition for these counterintuitive rolling and sliding scenarios by a simple comparison that will remind the reader that they have seen this concept in a more familiar context before. When a car slams on the brakes and skids to a stop the loss of the initial kinetic energy to heat is not dependent on the coefficient of friction. If the coefficient of friction increases the stopping distance decreases and vice versa but the kinetic energy lost to heat remains the same. The more mysterious aspect in the rolling/sliding cylinder is the exact factor 2/3 regardless of the coefficient of friction. We have programmed an app with App Inventor to simulate various scenarios in order to help develop the intuition which we have found lacking in students of physics and engineering.

Post-midnight irregularities in equator and its adjacent regions of topside ionosphere obtained by the CSES satellite

The post-midnight irregularities in equator and its adjcent regions, which have remained least explored so far, are investigated using the in situ electron density (Ne) measurements from 2019 to 2021 obtained by the China Seismo-Electromagnetic Satellite (CSES) in the topside ionosphere. The followings are salient points of our findings: 1) Equatorial and low latitude post-midnight irregularities are distributed along the dip equator, exhibiting a wavenumber 4 longitude variation pattern and a seasonal variation pattern characterized by peaks in summer and winter and valleys in spring and autumn during low solar activity (LSA) period in the topside ionosphere, 2) The longitude variation pattern of post-midnight irregularities aligns with that of the background Ne, with irregularities concentrated at the peaks of background Ne, 3) The occurrence rate of post-midnight irregularities increases significantly with higher solar flux, which can be attributed to the rapid increase in the background Ne under LSA conditions. Additionally, the growth rate of occurrence rate varies across different longitudes, with the Pacific and the Atlantic longitudes standing out as prominent regions, and 4) Post-midnight irregularities in the topside ionosphere may have two possible origins. One is irregularities generated at the lower altitudes that drift upward into the topside ionosphere, while the other is the irregularities generated directly in the topside ionosphere. The latter is supported by the presence of conditions conducive to irregularity generation in the topside ionosphere during midnight hours in LSA period. Given the scarcity of research on post-midnight irregularity compared to post-sunset irregularity, further investigations are essential to fully comprehend their generation mechanisms. The abundance of post-midnight Ne measurements from the CSES satellite offers a valuable opportunity for this research, which can enhance our understanding of post-midnight ionospheric dynamics, and their variations with solar activity.

Exploring reliable infrared object tracking with spatio-temporal fusion transformer

Currently, most thermal infrared (TIR) trackers rely on feature matching between the search image and a fixed template cropped from the first frame. Some Siam-based TIR trackers with a template update mechanism introduce historical prediction information in the temporal dimension through correlation filters. However, their feature characterization capability is inadequate to resist target scale variations, appearance changes, and occlusion. To address this challenge, we explore a novel spatio-temporal fusion Transformer (STFT) model to realize robust TIR object tracking. Our approach involves a Transformer-based encoder–decoder that fuses spatio-temporal information. Specifically, we design a dynamic template update strategy based on salient points feature(SPF) representation, which allows the model to leverage the most powerful spatio-temporal information by retrieving multiple salient points on the target image. To further fortify the dynamic template update strategy, we propose an IoU-Aware target state estimation head that utilizes the joint representation of target classification and localization. An IoU-Aware criterion is developed for quality estimation of the dynamic template. The proposed STFT-Net approach has been put to the evaluation on three challenging benchmarks, with extensive experimental results showcasing its superior performance in contrast to acclaimed tracking algorithms. The code is available at https://github.com/qinxin-wh/STFT-Net.