Performance Objectives Research Articles

Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO

The safety inspection system for the underside of vehicles demands both high detection speed and accuracy, necessitating a model with small parameters and high network efficiency. To address these issues and improve the real-time performance of under-vehicle safety inspection, the YOLOv8n object-detection algorithm was enhanced, resulting in the PSP-YOLO algorithm. Firstly, the improved PSP-YOLO network introduces a P2 small-object detection head, which is particularly effective in detecting tiny objects, such as small dangerous objects under vehicles, thereby significantly enhancing the ability to detect such small targets. Secondly, the Space-to-Depth Conv (SPD-Conv) module was introduced into the backbone network, which improves the detection performance of low-quality samples and small objects while ensuring high precision and accurate localization of small targets in images. Finally, the Partial Convolution (PConv) lightweight module was also added to the feature fusion network, effectively reducing the model’s parameters and computational load, conserving computing resources, and improving detection speed and accuracy. Experimental results demonstrate that the modified model increased mAP@0.5 by 3.9% and mAP@0.5-0.95 by 3.6% on the dataset used, reduced the number of parameters to 2.53 M, and achieved a detection speed of 104.6 f/s. Therefore, the improved YOLOv8n algorithm significantly enhances the detection performance of dangerous objects, meeting the high-precision and real-time requirements for vehicle underside safety inspections.

Measuring learning outcomes in cross-cultural relations courses: the case for experiential learning

Purpose Since accreditation bodies emphasize program-level learning goals, there is often less focus on measuring learning at the course level. This study aims to offer a method for using experiential learning exercises to measure student engagement and learning outcomes in cross-cultural relations courses by highlighting which learning outcomes to assess, how to measure the achievement of these learning outcomes, what students learn and the measurement process. Design/methodology/approach Data was collected from three sections of the (control group) and (experiment groups). Chi-square independence and analysis of variance (ANOVA) tests were employed to examine the significant association between these groups. Findings Students perform alike when exposed to various experiential learning methods. However, the lack of experiential learning might hinder students’ understanding of the course material. Consequently, the performance in both learning objectives from the control group was significantly lower than students in the experiment groups. Originality/value This study presents an approach for merging assurance of learning into integrated business courses, and how to use experiential learning exercises as instructional tools for improving and measuring learning. When course-level assessments focusing on developing relevant competencies are well-implemented, they can strengthen related program-level goals.

Return to Performance of a Soccer Player with an Adductor Longus Injury: A Case Report

Context: There is limited information on the quantification of external load and reconditioning programs during adductor longus injuries in soccer. Case Presentation: This case report describes a male professional soccer player (LaLiga) returning to performance following an adductor longus muscle injury during the 2022/2023 season. The player suffered the injury during a change of direction in a match. The injury was confirmed by ultrasound after 48 h, and the previously validated rehabilitation and reconditioning program was applied to the injured player. This case report has focused on the development of the on-field reconditioning program and the quantification of the load during this phase. The goal of this case report was to return the player to pre-injury loads using global positioning systems (GPS). Variables such as total distance, distances covered at different speeds and metabolic load variables were quantified during the injury process, with the aim of increasing them through training and reaching at least 75% of the game load. Therefore, objective performance criteria for making return-to-play decisions based on the use of GPS was determined. In addition, the return to play (RTP) was on the 20th day after the injury, and then four RTPs were recorded in the following 6 weeks after the injury occurred, without re-injury. Conclusions: The approach to the competition performance profile, through the quantification of the external load during the rehabilitation process of the injured player, allowed us a safe return to competition and continued competition with a 6-week follow-up.

Signs of consciousness in AI: Can GPT-3 tell how smart it really is?

The emergence of artificial intelligence (AI) is transforming how humans live and interact, raising both excitement and concerns—particularly about the potential for AI consciousness. For example, Google engineer Blake Lemoine suggested that the AI chatbot LaMDA might become sentient. At that time, GPT-3 was one of the most powerful publicly available language models, capable of simulating human reasoning to a certain extent. The notion of GPT-3 having some degree of consciousness could be linked to its ability to produce human-like responses, hinting at a basic level of understanding. To explore this further, we administered both objective and self-assessment tests of cognitive (CI) and emotional intelligence (EI) to GPT-3. Results showed that GPT-3 outperformed average humans on CI tests requiring the use and demonstration of acquired knowledge. However, its logical reasoning and EI capacities matched those of an average human. GPT-3’s self-assessments of CI and EI didn’t always align with its objective performance, with variations comparable to different human subsamples (e.g., high performers, males). A further discussion considered whether these results signal emerging subjectivity and self-awareness in AI. Future research should examine various language models to identify emergent properties of AI. The goal is not to discover machine consciousness itself, but to identify signs of its development, occurring independently of training and fine-tuning processes. If AI is to be further developed and widely deployed in human interactions, creating empathic AI that mimics human behavior is essential. The rapid advancement toward superintelligence requires continuous monitoring of AI’s human-like capabilities, particularly in general-purpose models, to ensure safety and alignment with human values.

Design of an effective multiple objects tracking framework for dynamic video scenes

Nowadays, the applications corresponding to video surveillance systems are getting popular due to their wide range of deployment in various places such as schools, roads, and airports. Despite the continuous evolution and increasing deployment of object-tracking features in video surveillance applications, the loopholes still need to be solved due to the limited functionalities of video-tracking systems. The existing video surveillance systems pose high processing overhead due to the larger size of video files. However, the traditional literature report quite sophisticated schemes which might successfully retain higher object detection accuracy from the video scenes but needs more effectiveness regarding computational complexity under limited computing resources. The study thereby identifies the scope of enhancement in traditional object-tracking functions. Further, it introduces a novel, cost-effective tracking model based on Gaussian mixture model (GMM) and Kalman filter (KF) that can accurately identify numerous mobile objects from a dynamic video scene and ensures computing efficiency. The study's outcome shows that the proposed strategic modelling offers better tracking performance for dynamic objects with cost-effective computation compared to the popular baseline approaches.

The Impact of Virtual Computer Screen Backgrounds on Subjective Performance Evaluation in Online Videoconferences

We investigate the influence of real and virtual computer screen backgrounds in Zoom videoconferences on subjective performance evaluation. Using an incentivized online experiment (N = 210), we systematically vary workers’ computer screen backgrounds in a supervisor–worker bargaining situation carried out in a videoconference. We find a significant influence of workers’ applied computer screen backgrounds on supervisors’ subjective performance evaluations, with significantly more positive ratings when workers use a virtual white background than a real white background (d = 0.47). This finding is robust when we control for workers’ measurable and salient objective performance and gender and for supervisors’ gender and age. Regarding the underlying processes, we show that supervisors’ perceptions of the workers’ likability and trustworthiness enhance subjective performance evaluations when virtual white videoconference backgrounds are applied. Practical implications for the application of human resource practices in digitalized environments are discussed.

The use of intervention mapping to guide the development of a school-based intervention to improve emergency medical services activation for stroke

BackgroundIf carried out correctly and without delay, activation of emergency services by stroke bystanders could improve mortality and disability from stroke. This paper describes the development of a school-based intervention using the Intervention Mapping approach. It aims to improve the appropriate activation of emergency medical services for suspected stroke by 12-15-year-old children.MethodsThe development of the intervention was guided by Intervention Mapping approach. The logic model of the problem was created through analysis of the existing literature and semi-structured interviews with stakeholders. Based on these findings, performance objectives and their determinants were determined and matched to create a model for changing emergency medical services activation behavior. Behavior change methods and their practical applications were then determined. Based on them, intervention messages and materials were designed, the intervention was drafted, pretested, and finalized.ResultsIt was found that the main performance objectives for the activation of emergency medical services were (1) recognizing symptoms, (2) communicating with the victim, and (3) calling an ambulance immediately. Their main determinants were knowledge, social influence, risk perception, self-efficacy, outcome expectations, and skills. Determinants were then matched with performance objectives to create the matrices of requested behavior changes. The following change methods were chosen: modeling, elaboration, belief selection, providing cues, scenario-based risk information, and cultural similarity. Methods were translated into practical applications in the form of a short educational film. The production company created, pretested, and finalized the film. As a result, a 5-minute entertainment-education video was created modeling an acute stroke with a child as the main bystander.ConclusionThe Intervention Mapping approach guided the development of a school-based program to improve Emergency medical services activation in stroke by 12–15 year old children. Our process and approach can serve as a model for researchers and health promotion professionals aiming to improve help-seeking behavior for stroke to improve stroke help-seeking behavior as well as other acute diseases.

First-in-man study of a novel everolimus-coated balloon for the treatment of coronary in-stent restenosis.

Treatment of patients with in-stent restenosis (ISR) remains challenging. In this setting the use of drug-coated balloons (DCB) represents an attractive approach to avoid adding another metal layer to the coronary wall. The Chansu Vascular Technologies (CVT)-ISR trial aimed to evaluate the safety and efficacy of a novel everolimus-DCB (CVT-DCB) using a new coating formulation and crystalline everolimus. The CVT-ISR trial was a prospective, multicenter, open, single-arm, first-in-man (FIM) study. A total of 51 patients (mean age 69.2 years, 74.5% male) with single ISR coronary lesions (≤24 mm in length) were enrolled at nine sites in Europe. The primary safety endpoint, freedom from target lesion failure (TLF) at 180 days, was 92.2%, with the lower bound of the 95% confidence interval (81.1%), above the protocol-defined objective performance criterion (OPC) (65% for conventional balloon angioplasty, P < 0.05). At 1 year freedom from TLF was 90.2%. The primary efficacy endpoint, in-stent late lumen loss at 180 days (evaluated in a predefined subgroup of 25 patients scheduled for late angiography), was 0.40 mm (median 0.30 mm), lower than the protocol-defined OPC of the plain balloon angioplasty historical control (0.80 mm, P < 0.001). This FIM study demonstrated the superior efficacy of the new everolimus CVT-DCB compared with conventional balloon angioplasty in the treatment of patients with ISR. NCT05731700.

ESL-YOLO: Small Object Detection with Effective Feature Enhancement and Spatial-Context-Guided Fusion Network for Remote Sensing

Improving the detection of small objects in remote sensing is essential for its extensive use in various applications. The diminutive size of these objects, coupled with the complex backgrounds in remote sensing images, complicates the detection process. Moreover, operations like downsampling during feature extraction can cause a significant loss of spatial information for small objects, adversely affecting detection accuracy. To tackle these issues, we propose ESL-YOLO, which incorporates feature enhancement, fusion, and a local attention pyramid. This model includes: (1) an innovative plug-and-play feature enhancement module that incorporates multi-scale local contextual information to bolster detection performance for small objects; (2) a spatial-context-guided multi-scale feature fusion framework that enables effective integration of shallow features, thereby minimizing spatial information loss; and (3) a local attention pyramid module aimed at mitigating background noise while highlighting small object characteristics. Evaluations on the publicly accessible remote sensing datasets AI-TOD and DOTAv1.5 indicate that ESL-YOLO significantly surpasses other contemporary object detection frameworks. In particular, ESL-YOLO enhances mean average precision mAP by 10% and 1.1% on the AI-TOD and DOTAv1.5 datasets, respectively, compared to YOLOv8s. This model is particularly adept at small object detection in remote sensing imagery and holds significant potential for practical applications.

When does betting on ambidexterity pay off? Exploring SME’s customer relationship strategies during crises

This study draws on the strategic fit framework and portfolio theory to show how ambidexterity in SMEs evolves in times of crisis. Using survey-based data and objective performance data collected at two points in time, we develop a framework that examines SMEs’ ambidextrous strategic orientation and customer retention-acquisition ambidexterity in relation to performance. This framework accounts for the interaction effects of customer portfolio management, proactiveness and customer concentration. The results show that an ambidextrous strategic orientation is positively related to retention-acquisition ambidexterity. Customer portfolio management can increase retention-acquisition ambidexterity when ambidextrous strategic orientation is low and vice versa. However, the importance of customer portfolio management is lower for SMEs with a strong proactive stance. Finally, the results show that customer concentration can undermine the effect of retention-acquisition ambidexterity on performance. This finding opens an interesting discussion on whether increasing retention-acquisition ambidexterity always benefits SME performance in times of crisis.

Validity of a virtual reality-based straight coloanal anastomosis simulator.

Current training methods for surgical trainees are inadequate because they are costly, low-fidelity, or have a low skill ceiling. This work aims to expand available virtual reality training options by developing a VR trainer for straight coloanal anastomosis (SCA), one of the Colorectal Objective Structured Assessment of Technical Skills (COSATS) tasks. We developed a VR-based SCA simulator to evaluate trainees based on their performance. To increase the immersiveness, alongside the VR headset, we used haptics as the primary method of interaction with the simulation. We also implemented objective performance metrics to evaluate trainee performance throughout the simulation. We presented our performance metrics to 27 participants for an Expert Consensus Survey (5-point Likert scale) and created weights for our metrics. The weighted average scores for the 24 task-specific metrics ranged from 3.5 to 5. Additionally, for the general metrics, the scores spanned from 3.3 to 4.6. In the second phase of our study, we conducted a study with 16 participants (novice n = 9, expert n = 7). Based on the performance, experts outperformed novices by 8.56% when referring to the total score (p = 0.0041). Three of the measurable metrics, purse suture (p = 0.0797), retracting the anvil (p = 0.0738), and inserting the colonoscope (p = 0.0738) showed a significant difference between experts and novices. Experts were smoother with their hand motions by 3.67% per second and took 70.77% longer paths to complete the same tasks. We created a high-fidelity coloanal anastomosis VR simulator. The simulator runs in real-time while allowing high immersion with a VR headset, deformable bodies, and a haptic device while providing objective feedback through performance metrics. Experts obtained higher scores throughout the simulation, including the quiz to demonstrate procedural knowledge, the metrics to demonstrate experience in steps/procedure, and control of their basic surgical skills and hand movements.

Thermal inactivation of non-proteolytic Clostridium botulinum spores in chilled plant-based foods

To support innovation in the dynamic plant-based foods sector, a study was conducted to set fit for purpose and risk-based alternative thermal processing conditions to the usually used safe harbour heat treatment of 90 °C - 10 min that ensures food safety of extended shelf life refrigerated products. To do so, inactivation data were collected to determine the thermal resistance properties (D- and z-values) of non-proteolytic Clostridium botulinum Type E spores (reference strain NCTC 8266) then build and validate specific inactivation models for two plant-based products: Konjac-based Seafood Analogue (Product 1) and Canola-based Eggs Analogue (Product 2) with desired sensorial properties not compatible with the application of the classical heat treatment of 90 °C - 10 min.D-values were determined at five temperatures ranging from 78 °C to 86 °C for each matrix to build the model and additional data were collected at 75 °C and 80 °C to validate the model. Dref80°C-values were estimated at 1.90 and 0.80 min and z-values at 6.12 °C and 6.84 °C respectively for products 1 and 2. Simulations were performed to estimate the time required to reach the required Performance Objective (PO) of 6 log reductions to ensure food safety at different temperatures for each product. The targeted PO could be reached in both products with lower temperatures and shorter times compared to the safe harbor. Results showed that the inactivation of non-proteolytic C. botulinum Type E spores was faster in product 2 compared to product 1. The developed and validated models, together with the proposed methodology can be used to support food industries in collecting specific data to justify setting fit for purpose heat treatments ensuring food safety with regards to the control of non-proteolytic C. botulinum type E spores without compromising on the desired sensorial properties such as those required for some plant-based products.

A month-long case-based bootcamp improves subjective and objective radiology knowledge for first-year radiology residents

PurposeFirst-year radiology residents enter residency with varying degrees of prior knowledge regarding radiology, which can be difficult for both trainees and physician educators looking to provide instruction. Recognizing this dilemma, we propose the adoption of a blended learning model PGY-2 radiology bootcamp at the start of training to give a structured foundation of imaging concepts. Methods20 total PGY-2 radiology residents spanning two cohorts of 10, one from 2022 and another from 2023, were included in a 4-week blended learning-style bootcamp at the beginning of their training. Effectiveness of the implementation was assessed using subjective Likert-scale surveys and an objective knowledge-based examination. ResultsSubjective confidence in basic radiology skills significantly improved for all modalities in the combined 2022-2023 results. Objective performance on the knowledge-based examination significantly improved in both cohorts, with no significant discrepancies in baseline scores between 2022 and 2023. ConclusionsWe recommend the implementation of a flipped-classroom, blended learning-style bootcamp for PGY-2 radiology residents. Our results demonstrated both subjective and objective improvement in radiology resident confidence and clinical knowledge, in addition to an early introduction to the institutional PACS.

Scale-adaptive salience supervision and dynamic token filtering for small object detection in remote sensing images

Abstract Recently, DETR-like detectors, which have shown remarkable performance in general object detection, face limitations when dealing with remote sensing images primarily containing small objects. Mainstream two-stage DETR-like models employ a pipeline that selects and processes a small portion of informative tokens, which enhances performance but also shows a high dependency on token selection. The current static token selection strategies lead to inconsistencies between the static selection criteria and dynamic token updates. Additionally, in remote sensing images, the limited information available for small objects and their inherent sensitivity to pixel shifts further degrade detection performance. To address this, we propose Scale-Adaptive Salience DETR (SAS DETR), a two-stage DETR-like method. SAS DETR incorporates dynamic token filtering, which uses a global threshold predictor to determine the token filtering ratio for each layer of the encoder. This approach selects an appropriate filtering ratio for different network layers while maintaining consistency between the foreground confidence map and token updates. Furthermore, we introduce a novel scale-adaptive salience supervision mechanism that adaptively scales the salience computation area based on object size, ensuring the model more effectively supervises small objects and utilizes the information within tokens without compromising the detection performance for objects of other sizes. Finally, we employ Scale-adaptive Intersection over Union to reduce the impact of pixel shifts on small objects. With these improvements, our proposed SAS DETR achieves 25.2% AP on the AI-TOD-V2 dataset with 24 training epochs and 50.4% AP on the COCO 2017 dataset with 12 training epochs.

Computational Biomimetics of Winged Seeds

We develop a computational pipeline to facilitate the biomimetic design of winged seeds. Our approach leverages 3D scans of natural winged seeds to construct a bio-inspired design space by interpolating them with geodesic coordinates in the 3D diffeomorphism group. We formulate aerodynamic design tasks with probabilistic performance objectives and adapt a gradient-free optimizer to explore the design space and minimize the expectation of performance objectives efficiently and effectively. Our pipeline discovers novel winged seed designs that outperform natural counterparts in aerodynamic tasks, including long-distance dispersal and guided flight. We validate the physical fidelity of our pipeline by showcasing paper models of selected winged seeds in the design space and reporting their similar aerodynamic behaviors in simulation and reality.

Do transportation infrastructure contracts enhance spending efficiency? Examining state highway construction programs

This study examines the impact of state highway construction contracts on state spending efficiency controlling for production structure, service demands, and situational factors. The theoretical argument is that because highway construction projects are relatively large in scale, complex, and can be monitored through objective performance measurement, state highway construction programs may save government production costs through contracts. Contracting helps highway producers achieve efficiency by optimizing production size based on workload and task complexity. The unit of analysis is 48 state governments’ highway construction contracts from 1998 to 2008. Through a two-stage analysis method including a Total Function Productivity (TFP) index and system dynamic panel data analysis, the results suggest that highway construction contracts enhance state highway spending efficiency, especially for large-scale construction projects.

Assessing the Impact of Force Feedback in Musical Knobs on Performance and User Experience

This paper examined how rotary force feedback in knobs can enhance control over musical techniques, focusing on both performance and user experience. To support our study, we developed the Bend-aid system, a web-based sequencer with pre-designed haptic modes for pitch modulation, integrated with TorqueTuner, a rotary haptic device that controls pitch through programmable haptic effects. Then, twenty musically trained participants evaluated three haptic modes (No-force feedback (No-FF), Spring, and Detent) by performing a vibrato mimicry task, rating their experience on a Likert scale, and providing qualitative feedback in post-experiment interviews. The study assessed objective performance metrics (Pitch Error and Pitch Deviation) and subjective user experience ratings (Comfort, Ease of Control, and Helpfulness) of each haptic mode. User experience results showed that participants found force feedback helpful. Performance results showed that the Detent mode significantly improved pitch accuracy and vibrato stability compared to No-FF, while the Spring mode did not show a similar improvement. Post-experiment interviews showed that preferences for Spring and Detent modes varied, and the applicants provided suggestions for future knob designs. These findings suggest that force feedback may enhance both control and the experience of control in rotary knobs, with potential applications for more nuanced control in DMIs.

The Making of Confident Surgeons: Why and How?

Low self-confidence in surgical residents can be associated with poor self-efficacy and perceptions of sub-optimal preparedness for practice at graduation. The influence of social and biologic determinants of confidence deserves further study. Through a randomized controlled trial of procedure-specific didactic and low fidelity simulation training for vaginal surgery, we showed positive correlations between self-confidence and objective performance in the real operating room for three different surgical procedures and through validated scales. This demonstrates an accurate ability of novice surgeons to self-monitor in a high-stakes environment. Our trial results (described in full elsewhere), combined with our multiple one-on-one teaching interactions with surgical trainees through the trial, incentivized us to evaluate self-confidence in view of optimizing it through directed training and feedback. The current opinion piece summarizes our main findings for surgical educators and emphasizes their role engaging with trainees at extremes of confidence.

Does diversity matter? Explaining the effects of team diversity on team performance using data from a student’s business simulation game

Purpose The purpose of this study is to determine the impact of different diversity criteria (age and gender) on team performance using a business simulation game, where performance is operationalized by market valuation (Tobin’s Q) and profitability (cumulative net income). Design/methodology/approach The authors collected data of a business simulation game at a German public university (128 student groups and 645 individuals). Using multiple linear regression models, they investigated the impact of the different diversity criteria on team performance. Findings The authors found no significant effects of gender diversity on market valuation and profitability. However, the results of this study indicate a positive relationship between age diversity and both performance variables. Originality/value This paper contributes to the debate on the relationship between diversity and team performance by using data from a business simulation game. This study adds value by considering different diversity factors, objective performance indicators and the endogeneity of team formation (the seminar leaders randomly assign students to teams) which has not been applied in similar studies yet. Because of the setting of a simulated business game, the results could also be applied to the real economy where we observe working teams every day.

A new era for stress research: supporting user performance and experience in the digital age

Stress is both a driver of objective performance impairments and a source of negative user experience of technology. This review addresses future directions for research on stress and ergonomics in the digital age. The review is structured around three levels of analysis. At the individual user level, stress is elicited by novel technologies and tasks including interaction with AI and robots, working in Virtual Reality, and operating autonomous vehicles. At the organisational level, novel, potentially stressful challenges include maintaining cybersecurity, surveillance and monitoring of employees supported by technology, and addressing bias and discrimination in the workplace. At the sociocultural level, technology, values and norms are evolving symbiotically, raising novel demands illustrated with respect to interactions with social media and new ethical challenges. We also briefly review the promise of neuroergonomics and emotional design to support stress mitigation. We conclude with seven high-level principles that may guide future work.