This study examines the effect of a corporate citizenship-oriented high performance work system (CC-oriented HPWS) and the interactive influence of CC-oriented HPWS and person-organization fit (PO fit) on creativity at the individual level. Our analysis of field data collected from 303 employees in two branch companies with a two-phase collection method provides empirical support for our theoretical model. The results indicate that high performance work system (HPWS) reflecting corporate citizenship (CC) correlates positively with individual creativity. In addition, the moderating effect of PO fit is significant in the relationship between CC-oriented HPWS and creativity at the individual level. On this basis, this paper discusses the theoretical and practical implications of these findings for future research.

Background: The proven efficacy of mTOR inhibitors (mTORI), tyrosine kinase inhibitors (TKI) or immune checkpoint inhibitors (ICI) therapies in metastatic renal cell carcinoma (RCC), suggested that these agents should be investigated as adjuvant therapy with the aim of eliminating any residual undetectable microscopic disease after curative resection. Our study aimed to compare the efficacy of these treatments through the application of an innovative method that reconstructs individual patient data. Methods: Nine phase III studies describing different treatment options for adjuvant RCC were selected. Individual patient data were reconstructed from Kaplan–Meier (KM) curves through the “IPDfromKM method”. Disease-free survival (DFS) were compared among combination treatments and control arm (placebo). Results were summarized as multi-treatment KM curves. Standard statistical testing was used, including hazard ratio and likelihood ratio tests for heterogeneity. Results: In the overall population, this study showed that two ICIs (pembrolizumab and nivolumab plus ipilimumab) and one TKI (sunitinib) showed superiority over placebo, whereas both TKI and mTORI did not. As we assessed DFS as the primary endpoint for the adjuvant comparison, the overall survival benefit remains unknown.. Conclusion: This novel approach to investigating survival has allowed us to conduct all of the indirect head-to-head comparisons between these agents in a context where no "real" comparative trials have been conducted.

Modular steel buildings (MSBs) interlocking inter-module connections (IMCs) have piqued researchers’ interest. However, the existing literature has not yet studied the optimisation of its plate thicknesses. This paper, therefore, focuses on optimising interlocking (IMCs) plate thickness in (MSBs), leveraging previous experimental and numerical simulation methodologies. Various numerical models for four MSBs interlocking (IMCs) with plate thicknesses (4 mm, 6 mm, 10 mm, and 12 mm) were developed under compression loading conditions. The study’s novelty lay in its comprehensive parametric analysis, which evaluated the slip phenomenon in (MSBs) connections and introduced a slip prediction model validated against empirical data. Further innovative machine learning approach was utilised to predict slip values based on applied force through a random forest regression model trained using the 'Treebagger' function. Sensitivity analysis and comparisons with alternative methods were utilised to underscore the reliability and applicability of the findings. The results showed that 11.03 mm was the optimal plate thickness for interlocking (IMCs) in modular steel buildings, with up to 8.08% reduced material costs. Increasing plate thickness boosts its deformation resistance by up to 50.75%. ‘TreeBagger’ random forest for anomaly detection and machine learning improves slip prediction up to 7% at higher force levels.

To understand forest dynamics under today’s changing environmental conditions, it is important to analyze the state of forests at large scales. Forest inventories are not available for all regions, so it is important to use other additional sources of information, e.g. remote sensing observations. Increasingly, remotely sensed data based on optical instruments and airborne LIDAR are becoming widely available for forests. There is great potential in analyzing these measurements and gaining an understanding of forests state. In this work, we combine the new generation radiative transfer model mScope with the individual-based forest model FORMIND to generate reflectance spectra for forests. Combining the two models allows us to account for species diversity at different height layers in the forest. We compare the generated reflectances for forest stands in Finland, in the region of North Karelia, with Sentinel-2 measurements. We investigate which level of forest representation gives the best results. For the majority of the forest stands, we generated good reflectances with all levels forest representation compared to the measured reflectance. Good correlations were also found for the vegetation indices (especially NDVI with R²=0.62). This work provides a forward modelling tool for relating forest reflectance to forest characteristics. With this tool it is possible to generate a large set of forest stands with corresponding reflectances. This opens the possibility to understand how reflectance is related to succession and different forest conditions.

In the context of partial autonomy, where autonomous vehicles and humans share control of the vehicle, bringing out-of-the-loop drivers back into the loop is a significant challenge. While warning signal design guidelines are commonly used to provide alerts, few studies have examined each signal in depth with an emphasis on the autonomous environment. This study aims to fill this gap by investigating visual, auditory, and tactile stimuli and modifying their sub-attributes to explore variations related to age, gender, and other individual backgrounds. A driving simulator was utilized to create a realistic driving environment and measure participants' reaction times in takeover request situations. Analysis of the data revealed correlations between age and reaction times for auditory and tactile signals, with interaction effects observed between age and sub-attribute intensity. Additionally, participants exhibited varying reaction time patterns in response to different sub-attribute intensities. By evaluating individual differences in perception based on modality characteristics, often overlooked in prior research, this study serves as a foundational contribution to future research in the field.

Abstract:The objects of fine-grained image categories(e.g., bird species) are various subclass under different categories. Because the differences between subclass are very subtle and most of them are concentrated in multiple local areas, the task of fine-grained image recognition is very challenging. At the same time, some fine-grained networks tend to focus on a certain region when judging the target category, resulting in the lack of other auxiliary regional features. To this end, Inception V3 is used as the backbone network, and an enhanced and complementary fine-grained image classification network is designed. While adopting the method of reinforcement learning to obtain more detailed fine grain image features, the complementary network can obtain the complementary discriminant area of the target through the method of attention erasure to increase the network's perception of the overall target. Finally, experiments are conducted on CUB-200-2011, FGVC Aircraft and Stanford dogs three open datasets. The experimental results show that the proposed model has better performance.

The Philippines has the highest HIV incidence rate in the Asia-Pacific region. Previous studies identified multilevel factors contributing to this phenomenon. This scoping review describes the extent of the literature on HIV contributory factors and identifies gaps to guide directions for HIV prevention research. The keywords HIV incidence, individual, sociocultural, environmental factors, and Philippines, were used to search for evidence on Google Scholar, PubMed, Scopus, and Web of Science databases. A total of 1,017 peer-reviewed articles published in the English language from 2014 to 2024 were reviewed using the Joanna Briggs Institute Scoping Review protocol. Thirty articles were included in this review. The majority of the available evidence was quantitative studies published between 2020 and 2024. Individual, sociocultural, economic, educational, political, and environmental factors shaped the Philippines’ HIV epidemic. Low levels of HIV knowledge, low testing rates, high-risk sex behaviors, stigma, lifestyle, location, lack of HIV education in schools, conservative society, and restrictive policies are HIV contributing factors. The Philippine HIV epidemic is multifactorial. Identifying the contributory factors is crucial in developing targeted interventions for high-risk populations. A dedicated HIV research program is needed to provide evidence-based information to guide the development of HIV preventive programs and research.

Well-being is one of the pillars of positive psychology, which is known to have positive effects not only on the personal and professional lives of individuals, but also on teams and organizations. Understanding and promoting individual well-being is essential for staff health and long-term success, but current tools for assessing subjective well-being rely on time-consuming surveys and questionnaires, which limit the possibility of providing the real-time feedback needed to raise awareness and change individual behavior. This paper proposes a framework for understanding the process of non-verbal communication in teamwork, using video data to identify significant predictors of individual well-being in teamwork. It relies on video acquisition technologies and state-of-the-art artificial intelligence tools to extract individual, relative, and environmental characteristics from panoramic video. Statistical analysis is applied to each time series, leading to the generation of a dataset of 125 features, which are then linked to PERMA (Positive Emotion, Engagement, Relationships, Meaning, and Accomplishments) surveys developed in the context of positive psychology. Each pillar of the PERMA model is evaluated as a regression or classification problem using machine learning algorithms. Our approach was applied to a case study, where 80 students collaborated in 20 teams for a week on a team task in a face-to-face setting. This enabled us to formulate several hypotheses identifying factors influencing individual well-being in teamwork. These promising results point to interesting avenues for research, for instance fusing different media for the analysis of individual well-being in teamwork.

Understanding morphological and genetic diversity of forest trees is essential to facilitate their conservation, sustainable management and genetic improvement for addressing the economic and environmental demands and challenges. We have examined variation in morphometric seed and pod traits and seed germination of multipurpose and widely distributed forest tree Albizia lebbek (Indian siris) from 12 provenances covering the species’ range in northern India and its relationships with bioclimatic factors. Highly significant inter-provenance variation was observed in seed size (seed length (SL), seed width (SW), SW/SL, SL×SW), seed mass (1000 seed weight), pod size (pod length (PL), pod width (PW), PW/PL), seed germination, number of seeds per pod, and insect infected seeds (IIS). The provenance effect contributed a major portion to the total phenotypic variance. Morphometric variation in all traits except for IIS was not clinal related to longitude, latitude, altitude or rainfall. There was an indication of the presence of ecotypic variation. IIS showed clinal variation with significant negative correlation with latitude. Most seed size and pod size traits showed positive correlations with several temperature bioclimatic factors and negative correlations with several precipitation bioclimatic factors. Our results have significance for genetic improvement, population genetics and genomics studies and conservation and sustainable management of A. lebbek genetic resources.

Facial expression recognition is crucial for understanding human emotions and nonverbal communication. With the growing prevalence of facial recognition technology and its various applications, accurate and efficient facial expression recognition has become a significant research area. However, most previous methods have focused on designing unique deep-learning architectures while overlooking the loss function. This study presents a new loss function that allows simultaneous consideration of inter- and intra-class variations to be applied to CNN architecture for facial expression recognition. More concretely, this loss function reduces the intra-class variations by minimizing the distances between the deep features and their corresponding class centers. It also increases the inter-class variations by maximizing the distances between deep features and their non-corresponding class centers, and the distances between different class centers. Numerical results from several benchmark facial expression databases, such as Cohn-Kanade Plus, Oulu-Casia, MMI, and FER2013, are provided to prove the capability of the proposed loss function compared with existing ones.

The inter-tip times (ITTs) of tipping-bucket rain gauges potentially provide the highest-resolution intensity data that can be acquired from this type of gauge. At an intensity of 100 mm h-1, a typical gauge with a sensitivity of 0.2 mm of rainfall would be expected to tip every 7.2 seconds. However, TBRGs are often equipped with syphons, to reduce the dynamic calibration error that results from continued (and unmeasured) inflow to a bucket as it tips. This increases the accuracy of rainfall depth recording, but the time to fill and empty the syphon can reduce the ability of a TBRG to respond to (and for the ITTs to reflect) short-term intensity fluctuations. This ability is already limited by the discretisation arising from the filling and emptying of the buckets themselves. Laboratory tests with controlled water inflow rates were performed using two high-quality TBRGs, one a 'straight-through' design and the other syphon-equipped. These confirmed that at all intensities at which the syphon operates, a regular sequence of fixed-duration ITTs (such as the 7.2 s mentioned above) does not occur. Rather, the ITTs are perturbed by the syphon cycling. The gauges were also co-located in the field, and linked to carefully synchronised event data loggers. Data collected during several rainfall events revealed differences in the ITTs, and again confirm that the ITT sequence of a syphon-equipped TBRG exhibits artefacts related to syphon operation, that are not present in the 'straight-through' data. These artefacts can result in ITT differences of many minutes, depending on the rainfall intensity, and are problematic for the use of ITTs to estimate intensity. Peaks and troughs in the intensity profile also differed between the two gauges. It is recommended that in the application of TBRGs for studies where short-term intensity data are required, 'straight-through' gauges should be used, and syphon-equipped gauges avoided.

The objective of this study was to investigate the relationship between high-performance work systems and a company’s social performance, taking into account the organizational climate as a situational variable. Specifically, this study aimed to analyze the impact of perceptions of a high-performance work system on the perception of a company’s social responsibility (CSR) among employees of Jordanian companies with an organizational size of 300 or more. This study used 175 valid questionnaires, and SPSS and AMOS 24 were used to test the hypotheses. The results showed that there was a positive relationship between the perception of a high-performance work system and the perception of a company’s CSR. Furthermore, this study found that a positive diversity climate could enhance the positive relationship between the two. These findings suggest that high-performance work systems can contribute to the development of sustainable human resource management systems and that a positive diversity climate is essential in shaping these systems. Based on these results, it is recommended that companies implement HPWSs and encourage workforce diversity to maximize the value of CSR activities and ensure their sustainability.

In this article, we present two topics related to mathematical economics. We offer an intriguing proposal for securing cost and production functions in the face of uncertainty and risk. We discuss the relationship between individual and social preferences. This is important because traditional revealed preference theory does not account for such factors.

Pig farms management is involved in multiple factors such as environmental conditions, animal behavior, weight gaining and others. In all those scenarios, it is fundamental to detect each individual and its development as it grows to reach the shipment phase. Therefore, the objective of this study was to apply an automatic individual pig detection algorithm using Deep Learning Techniques, in frames taken from videos recorded during the weaning phase in real production conditions of small and medium producers in tropical conditions in Colombia. The weaning phase was selected because individual detection in each cage is more difficult due to environmental adaptations that piglets undergo after nursing. Piglets tend to cluster together during this phase, making image processing more challenging compared to the fattening phase, where animals are more isolated. The You Only Look Once (YOLOv7) algorithm and VGG Image Annotator were used to detect individuals in two pens separated by gender. All animal postures, lighting conditions were considered. The Precision, Recall and mAP metrics were used to assess the model performance with different confidence values. A precision of 98.3% in a threshold of 0.9 was reached, Recall of 98.5% in a threshold of 0.85. Data augmentation techniques were important to increase the dataset and to have a more realistic model for individuals’ detection. The method used allowed us to identify all the complete pigs’ bodies under different posture and lighting situations.

Recent technological advancements in conservation genetics and genomics have resulted in diverse tools for aiding conservation of species. The precision and resolution of high throughput sequencing technologies provide valuable insights to aid conservation decisions, but these technologies are often financially unfeasible or unavailable in resource constrained countries. Inter-Simple Sequence Repeat (ISSR) markers, when combined with sensitive automated detection systems, provide a simple, cheap means to investigate genetic diversity and discriminate closely related species. Here we apply this technology to assess genetic diversity and taxonomic delimitation in the Encephalartos eugene-maraisii species complex, a highly threatened, taxonomically dubious group of cycads in South Africa. Silica dried cycad leaflets were extracted using the Oktopure robot employing Sbeadex chemistry and amplified with fluorescently tagged ISSR primers. Band presence was scored with an ABI 3130 genetic sequencer under three different fluorescence cut-off values. Phenetic analyses were performed in NTSYS software while Bayesian analysis was performed in STRUCTURE, and haplotype network analysis, AMOVA and Tajima’s D statistic was computed with PopART. Our analyses support taxonomic singularity of E. dyerianus, E. dolomiticus and E. eugene-maraisii. Relationships between E. nubimontanus and E. cupidus remain uncertain. E. middelburgensis samples showed no clustering but had poor amplification success. This study demonstrated the suitability of automated ISSR fingerprinting as a method for plant conservation studies, especially in resource-constrained countries.

Abstract: Although experimental attempts to measure a single molecule/particle at room temperature or under physiological conditions without immobilization, for example on a surface, or without hydrodynamic flow have so far failed, this failure has given impetus to the underlying theory of Brownian motion towards its stochastic due to diffusion. Quantifying the thermodynamic jitter of molecules/particles inspires many and forms the theoretical basis of single-molecule/single-particle biophysics and biochemistry. For the first time, our simulation results for a live cell (cytoplasm) show that the tracks of individual single molecules are localized in Brownian motion, while there is fanning out in fractal diffusion (anomalous diffusion).

Considering the multifaceted nature of neurodegenerative diseases like dementia and the neces-sity for interprofessional knowledge, this research extends its scope to encompass professionals with diverse levels of training and experience in dementia care. A need analysis for the project “My INdividual Digital EDucation.RUHR” (MINDED.RUHR) is conducted to develop an au-tomatized recommender system for individual learning content using AI. In this sub-study, the aim was to reveal didactic specialties, knowledge gaps and structural challenges of further edu-cation in dementia care of different health professions and to derive learning preference personae. Eight focus group interviews among nine health professions and up to six participants (n = 34) each took place to survey distinct didactic experiences and learning needs. The results reflect various learning preferences, with a propensity to multimedia, practical and interactive tasks. Health professions are used to digital education but show aversions against certain e-learning formats. The derived learning preference personae constitute profound blueprints for a us-er-centred digital education design process, aiming to establish personalized and representative further education in dementia care applicable to various individual preferences and structural workplace challenges of healthcare professions.