Miracle-dz/UniFAR-FTUs huggingface dataset:
Productive losses on broiler preslaughter operations: effects of the distance from farms to abattoirs and of lairage time in a climatized holding area

This work aimed to assess the effects of preslaughter lairage time on the welfare and mortality of broilers transported in different farm-abattoir distances. It was evaluated 215 poultry transport trucks from a commercial poultry slaughterhouse from 2006 to 2007. Data were obtained from the holding area acclimatized through fans and water misting in the mornings, afternoons and at night. The thermal variables (temperature and relative humidity), distance, lairage time and density of birds per cage were considered in the analysis. The effects of distance and lairage time were important in the variation of rectal temperature of the animals and on the number of dead animals per truck. Two models were developed (mean and dispersion), for each response variable in function of the interaction between lairage time and distance. As the lairage time in the holding area increased, the rectal temperature of the birds was reduced for all the studied distances. In farm-abattoir distances higher than 25 km, mortality was low when lairage time was lower than 1 hour. However, for distances below 25 km, in the same time interval, the number of dead birds was two-fold higher than in the first situation. The adjustment of lairage time has to be done accordingly to the distance provided that the lairage environment is correctly climatized.
Interactive Objects For Augmented Reality By Using Oculus Rift And Motion Sensor

Augmented Reality (AR) is a technology to blend the digital computer such as audio, text, animation, 3D models seamlessly to the real-world environment. This technology is going to change the way how people imagine, see and learn in the future. This paper discussed the implementation and development of interactive objects for AR by integrating Oculus Rift, Leap Motion Controller (LMC) and camera. In this project, a video-displayed AR device is created to work together with a special designed AR book. A LMC is essential as a peripheral input device for the user to interact with the system. The application of LMC further enhances the interactivity with different well-designed hand gestures such as thumb up, down and pinching. The proposed design will enhance the user experience for interacting, engaging and responding to the information.
Fall composition of storage lipids is associated to the overwintering strategy of Daphnia 1 2

29 30 Diapause, using dormant eggs, is a strategy some zooplankton use to avoid winter 31 months of persistent low temperatures and low food availability. However, reports of 32 active zooplankton under the ice indicate other strategies also exist. This study aimed at 33 evaluating whether fall composition of storage lipids is different between diapausing and 34 actively overwintering Daphnia. We assessed the quantity of storage lipids and fatty acid 35 composition of Daphnia species along with fatty acid content of seston in six boreal, 36 alpine and subarctic lakes at the onset of winter, and evaluated the association of storage 37 lipids to Daphnia overwintering strategy. We found that active overwintering Daphnia had 38 >55% of body fat and the highest fatty acid concentrations. Polyunsaturated fatty acids, 39 especially stearidonic acid (18:4n3; SDA) and high ratios of n3:n6 were preferentially 40 retained in active overwintering Daphnia compared to those that entered diapause. The 41 Daphnia fatty acid compositions were independent of that of the seston diet, indicating 42 Daphnia adjusted their storage lipids according to the physiological requirements of a 43 given overwintering strategy. The occurrence of the active overwintering strategy has 44 consequences to zooplankton community structure, and can have important implications 45 for the transfer of high quality energy in higher trophic levels. 46 47
Role of academia-industry collaboration in enriching engineering education: A case study in sub-sahara context

Education and training stays one of the key parts of technical institutions, particularly in sub-Sahara nations where the absence of talented laborers is a noteworthy bottleneck impeding the intensity and imaginative limit of firms. Building instruction is deficient with regards to an association amongst information and certifiable issues. Building training as a human undertaking is likewise confronting various extra difficulties of securing, and reception of information, advancement and innovation exchange, fortifying the instructive venture and occupation manifestations. Abilities improvement has been, and still is, an argumentative issue in the scholarly world and in addition mechanical segment. Presently days, the greater part of the universities are confronting the issue of absence of viable learning and experience among their lecturers who have joined the instructing soon after completing their graduation. This paper talks about the necessity of a compelling collaboration amongst the scholarly world and industry which has turned into an undeniably a basic part of productive national development frameworks. Industry encounter enables instructive projects to grow in inventive ways. The contextual investigation examined, demonstrates that such collective endeavors can prompt expanded organization with researchers from various and interdisciplinary foundations.
FDI SPILLOVERS IN TRANSITIVE ECONOMIES INTEGRATING INTO THE EU IN CASE OF THE BALTIC COUNTRIES EFEKTY POŚREDNIE BIZ W GOSPODARKACH PRZEJŚCIOWYCH INTEGRUJĄCYCH SIĘ Z UE NA PRZYKŁADZIE KRAJÓW BAŁTYCKICH

Summary: The paper deals with the idea of spillovers (or indirect effects) from foreign direct investment increasing the firm productivity in transitive economies under the circumstances of economic integration. The Baltic countries are the best example of such a kind of states. The indirect effects for the productivity are examined both at a country and at a firm level with the use of a proved estimation model in order to probe for the existence, sign and magnitude of different types of spillovers. The paper includes a brief historical analysis of the previous works and the history of elaboration of an estimation model for spillovers. The analysis of FDI inflows in Estonia, Latvia and Lithuania with diagrams is included to visualize the change in the attitude of foreign investors to these countries during the post-soviet period and after their accession to the EU.
Analysis of experimental reflection spectra from diffraction gratings using the Rayleigh theory and a rigorous method

We obtain the reflection spectra of p-polarized light from metallic gratings fabricated by means of the interference technique. The gratings have constant period and heights are varied by changing the exposure time of the interference pattern on the sample. The experimental spectra show two minima due to the excitation of surface plasmons, and they are compared with the corresponding numerical results obtained with two models, the Rayleigh theory and the rigorous integral method. In order to demonstrate the validity of the Rayleigh hypothesis the amplitudes of the gratings are chosen close to the Rayleigh limit.
Introducing hardware in the loop and model based simulation concepts in the ALMA observatory for software testing

The Atacama Large Millimeter /sub-millimeter Array (ALMA) has been working in operations phase regime since 2013. The transition to the operations phase has changed the priorities within the observatory, in which, most of the available time will be dedicated to science observations at the expense of technical time required for testing newer version of ALMA software. Therefore, a process to design and implement a new simulation environment, which must be comparable - or at least- be representative of the production environment was started in 2017. Concepts of model in the loop and hardware in the loop were explored. In this paper we review and present the experiences gained and lessons learned during the design and implementation of the new simulation environment.
Theory of noise in semiconductor lasers in the presence of optical feedback

We derive analytical expressions for the power spectral densities of intensity and frequency noise of single mode semiconductor lasers in the presence of an optical feedback. By explicitly taking into account the spontaneous emission processes into the laser mode, we obtain a behavior which, at high frequencies significantly differs from the usual one, and is in good agreement with recent experimental results. In particular, we are able to show in which way the intensity and frequency noise, besides being influenced by the external cavity length, are affected by the presence of the well known resonant peaks in the noise spectra of the solitary laser and how a substantial lowering and flattening in the noise spectra can be obtained with external cavity round trip times shorter than the inverse of the peak resonant frequency. We also present experimental results in good agreement with the theoretical ones.
The nonaxisymmetric baroclinic instability in thin accretion disks

The instability of linear, nonaxisymmetric baroclinic waves in the radiative regions of a thin accretion disk is investigated with simplifying assumptions used in the representation of the structure of the basic state. As in stars with rotation laws deviating from rotation on cylinders, the departure from barotropy (''baroclinicity'') resulting from such rotation feeds potential energy from the basic state into the kinetic energy of the baroclinic motions. The baroclinic instability is considered as a possible mechanism for driving turbulence in disks. Estimations of the Reynolds stress from baroclinic waves indicate that the degree of baroclinicity in stationary disk models is insufficient to account for the large viscosity inferred from observations of, e.g., cataclysmic variables.
Alizarin red S staining of the crustacean cuticle: implementation in the study of Porcellio scaber larvae

Exoskeletal cuticle of crustaceans is a chitinous matrix, produced apically by epidermis and stiffened by sclerotization and calcification. Embryos of terrestrial isopod crustacean Porcellio scaber develop within the female brood pouch, marsupium, and after hatching larvae mancae continue their development in the marsupium for another week. This study was performed to reveal at the histochemical level whether the exoskeletal cuticle of marsupial mancae is already calcified. Fifteen different procedures of histochemical staining with alizarin red S (ARS), established for calcified tissue localization primarily in vertebrate histology, were evaluated on mancae and adult P. scaber specimens. The best differential staining of the the exoskeletal cuticle was obtained by neutral buffered formaldehyde fixation, followed by paraffin sections staining with ARS 1 (pH 9) or ARS 2 (pH 6.4) or ARS 3 (pH 4.8)solution. Clear differential staining was achieved also in cryosections of formaldehyde fixed samples, stained with ARS 1 solution (pH 9). Our results suggests that prominent calcification of exoskeletal cuticle is present during postembryonic development of P. scaber mancae in the marsupium. Exoskeleton hardening is likely important also for body movements, that we observed in mancae before they are released from marsupium. The proposed procedures of ARS method are presumed to be applicable for histochemical studies of other calcified chitinous matrices.
Fog Computing Challenges: A Systematic Review

Internet of Things (IoT) applications continue to grow at a rapid scale. However, current cloud centric IoT architectures are not feasible to support the mobility needs as well as latency requirements of time critical IoT applications. This has restricted the growth of IoT in certain sectors. This paper investigates the fog-computing paradigm as an alternative for IoT applications. There is a need to systematically review and synthesize fog computing concerns or challenges for IoT applications. This paper aims to address this important research need using a well-known systematic literature review (SLR) approach. Using the SLR approach and applying customized search criteria derived from the research question, 17 relevant studies were identified and reviewed in this regard from an initial set of 439 papers. In addition, 4 papers were manually identified based on their relevance. The data was organized into four major challenge categories. The findings of this research paper can help practitioners and researchers to understand fog computing related concerns, and provide a number of useful insights for future work. The scope of this paper is limited to the number of reviewed studies from chosen database.
The Impacts of COVID-19 on Black, Indigenous, Artists and Arts Administrators of Color in the U. S.

Abstract This study investigated the impacts of COVID-19 on Black, Indigenous, and artists and arts administrators of Color. We addressed the primary research question, in what ways has COVID-19 impacted BIPOC artists and arts administrators? We also answered the secondary research question, do differences in impacts exist in health and wellness for BIPOC artists and administrators based on demographic characteristics? By centering BIPOC artists and arts administrators in this study, we initiated an area of critical scholarly inquiry that explored the impacts of COVID-19 on creatives who identify as historically marginalized and oppressed based on their race in U. S. society.
Emotions Matter: Towards Personalizing Human-System Interactions Using a Two-layer Multimodal Approach

Monitoring and predicting user task performance is critical as it provides valuable insights for developing personalized human-system interactions. Key factors that impact task performance include cognitive workload, physiological responses, and affective states. However, a lack of consideration of any of these factors could lead to inaccurate task performance prediction because of their interplay. To address this challenge, we developed a novel hierarchical machine learning approach that considers these three factors to predict task performance. We exposed twenty-eight participants to a two-step experimental study. The first step aimed to induce different affective states using a validated video database. The second step required participants to perform validated low and high cognitive workload-inducing tasks. To evaluate the performance, we compared the models developed using the hierarchical approach that uses emotional and physiological information, to models that use only physiological information. We observed that our proposed approach always outperformed the models that only use physiological information to predict task performance by achieving a better average person independent mean absolute error. However, information gained across various models using the hierarchical approach was not linear. Additionally, we found that the top predictors for each model varied, and the model with the highest information gain included emotional features. These findings suggest the importance of choosing the appropriate machine learning model and predictors for building robust models for predicting task performance.
Privacy Preserving Personalization for Video Facial Expression Recognition Using Federated Learning

The increased ubiquitousness of small smart devices, such as cellphones, tablets, smart watches and laptops, has led to unique user data, which can be locally processed. The sensors (e.g., microphones and webcam) and improved hardware of the new devices have allowed running deep learning models that 20 years ago would have been exclusive to high-end expensive machines. In spite of this progress, state-of-the-art algorithms for facial expression recognition (FER) rely on architectures that cannot be implemented on these devices due to computational and memory constraints. Alternatives involving cloud-based solutions impose privacy barriers that prevent their adoption or user acceptance in wide range of applications. This paper proposes a lightweight model that can run in real-time for image facial expression recognition (IFER) and video facial expression recognition (VFER). The approach relies on a personalization mechanism locally implemented for each subject by fine-tuning a central VFER model with unlabeled videos from a target subject. We train the IFER model to generate pseudo labels and we select the videos with the highest confident predictions to be used for adaptation. The adaptation is performed by implementing a federated learning strategy where the weights of the local model are averaged and used by the central VFER model. We demonstrate that this approach can improve not only the performance on the edge device providing personalized models to the users, but also the central VFER model. We implement a federated learning strategy where the weights of the local models are averaged and used by the central VFER. Within corpus and cross-corpus evaluations on two emotional databases demonstrate that edge models adapted with our personalization strategy achieve up to 13.1% gains in F1-scores. Furthermore, the federated learning implementation improves the mean micro F1-score of the central VFER model by up to 3.4%. The proposed lightweight solution is ideal for interactive user interfaces that preserve the data of the users.
A Spatio-temporal Learning for Music Conditioned Dance Generation

The music-conditioned dance generation, i.e., dancing to music, is a usage scenario of multi-modality human motion synthesis. Typically, it is a challenge to choreograph continuous motions coinciding with the melody and rhythm of the music. This paper proposes a position-wise encoding-decoding framework for spatio-temporal learning of motions and long-term skeleton-based dance generation oriented on music. Given the positional embedding of the frames in 1-minute video clips, firstly, we modularize a regional attention-based feed-forward mechanism to encode the music features. Secondly, based on the skeleton of each frame and the joint trajectories across motion frames, we formalize a graph topology to represent each dance sequence’s spatial and temporal knowledge. Specifically, we propose a graph convolutional network (GCN) based blocks to process long-term dependencies of motions and leverage the spatial and temporal features. Both music and motion paths are learned fully in positional embedding schemes and constructed by repeating the corresponding blocks. Finally, as the task of dance generation is inherently the consistency between music and motions, we proposed a cross-modality feature fusion for multimodal interaction and music-conditioned dance generation. Experimental results demonstrate that our method outperforms state-of-art methods in motion quality and motion-music correlation metrics.
Leadership and credition: Followers' neural response to leaders who are perceived as transformational

COPYRIGHT © 2022 Bergner, Rybnicek and Koschutnig. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Leadership and credition: Followers’ neural response to leaders who are perceived as transformational
Comfortability Recognition from Visual Non-verbal Cues

As social agents, we experience situations in which sometimes we enjoy being involved and others where we desire to withdraw from. Being aware of others’ “comfort towards the interaction” help us enhance our communications, thus this becomes a fundamental skill for any interactive agent (either a robot or an Embodied Conversational Agent (ECA)). For this reason, the current paper considers Comfortability, the internal state that focuses on the person’s desire to maintain or withdraw from an interaction, exploring whether it is possible to recognize it from human non-verbal behaviour. To this aim, videos collected during real Human-Robot Interactions (HRI) were segmented, manually annotated and used to train four standard classifiers. Concretely, different combinations of various facial and upper-body movements (i.e., Action Units, Head Pose, Upper-body Pose and Gaze) were fed to the following feature-based Machine Learning (ML) algorithms: Naive Bayes, Neural Networks, Random Forest and Support Vector Machines. The results indicate that the best model, obtaining a 75% recognition accuracy, is trained with all the aforementioned cues together and based on Random Forest. These findings indicate, for the first time, that Comfortability can be automatically recognized, paving the way to its future integration into interactive agents.
A Gap in Games Research: Reflecting on Two Camps and a Bridge

This paper discusses the divided nature of games research, and how most of the research effort can be placed in camps located on each side of a sliding, one-dimensional scale. A middle ground is slowly being bridged between these two camps, which is filling in the gaps between these two community. The paper presents a definition of this bridging research, and how we as a field can make our research more impactful by continuing this bridging of the space between the two camps. It also represents a call to action for more scientists to perform inter- and transdisciplinary research in order to further increase the impact of games research.
Keep in Touch: Combining Touch Interaction with Thumb-to-Finger µGestures for People with Visual Impairment

We present a set of 8 thumb-to-finger microgestures (TTF µGestures) that can be used as an additional modality to enrich touch interaction in eyes-free situations. TTF µGestures possess characteristics especially suited for people with visual impairment (PVI). They have never been studied specifically for PVI to improve accessibility of touchscreen devices. We studied a set of 33 common TTF µGestures to determine which are feasible and usable without seeing while the index is touching a surface. We found that the constrained position of the hand and the absence of vision prevent participants from being able to efficiently target a specific phalanx. Thus, we propose a set of 8 TTF µGestures (6 taps, 2 swipes) balancing resiliency (i.e., low error-rate) and expressivity (i.e., number of possible inputs): as a dimension combined with the touch modality, it would realistically multiply the touch command space by eight. Within our set of 8 TTF µGestures, we chose a subset of 4 µGestures (2 taps and 2 swipes) and implemented an exploration scenario of an audio-tactile map with a raised-line overlay on a touchscreen and tested it with 7 PVI. Their feedback was positive on the potential benefits of TTF µGestures in enhancing the touch modality and supporting PVI interaction with touchscreen devices.