When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project.

Holographic subsurface radar (HSR) is currently not in widespread usage. This is due to an historical perspective in the ground penetrating radar (GPR) community that the high attenuation of electromagnetic waves in most media of interest, and the inability to apply time-varying gain to the continuous wave (CW) HSR signal precludes sufficient effective penetration depth. While it is true that the fundamental physics of HSR, with its use of a CW signal, does not allow amplification of later (i.e. deeper) arrivals in lossy media (as is possible with impulse subsurface radar — ISR), HSR has distinct some distinctive advantages. The most important of these is the ability to do shallow subsurface imaging with a resolution that is not possible with ISR. In addition, the design of an HSR system is simpler than for ISR due to the relatively low-tech transmitting and receiving antennae. This paper provides a review of the main principles of HSR through an optical analogy and describes possible algorithms for radar hologram reconstruction. We also present a review of the history of development of systems and applications for HSR of the “RASCAN” type which is possibly the only holographic subsurface radar that is produced in lots. Among the subsurface imaging and remote sensing applications considered are humanitarian demining, construction inspection, surveys of historic architecture and artworks, nondestructive testing of dielectric aerospace materials, security applications, paleontology, detection of wood-boring insect damage, and others. Each application is illustrated with relevant data acquired in laboratory and/or field experiments.

Artificial Intelligence (AI) has the power to generate scenarios and make predictions through the use of advanced algorithms and machine learning techniques. OpenAI’s GPT-3 AI is a state-of-the-art language model that has been trained on a large dataset of text, which allows it to generate hu-man-like text, and it can generate scenarios for different fields. However, GPT-3 was trained on data available up until June 2021, had no access to more recent data nor was connected to the internet. In this study, we investigated the capability of OpenAI’s GPT-3 AI to predict the Ukrainian war es-calation, which started in 2022 and had massive geopolitical effects. We used GPT-3´s capability to generate future scenarios, to check those scenarios for internal consistency, and to create a proba-bility estimate. The results showed that although GPT-3 described an open war as one of the low probability scenarios, its capability on predicting the future was limited. Furthermore, it became evident that checking internal consistency of the generated scenarios could be improved. We ap-preciated GPT-3 as very useful and powerful for generating future scenarios, but also concluded that its prediction capabilities of real-world events are limited, should be used with caution, and require further development.

This paper examines the potential of Artificial Intelligence (AI) to address societal megatrends, with a specific focus on OpenAI’s Generative Pre-Trained Transformer 3 (GPT-3). To do this, we con-ducted an analysis using GPT-3 in order to explore the benefits of AI for digitalization, urbanization, globalization, climate change, automation and mobility, global health issues and aging population. We also looked at emerging markets as well as sustainability in this study. Interaction with GPT-3 was done solely through prompt questions and generated responses were analyzed. Our results indicate that AI can significantly improve our understanding of these trends by providing insights into how they develop over time and what solutions could be implemented. Further research is needed to determine how effective AI will be in addressing them successfully but initial findings are encouraging. Our discussion focuses on the implications of our findings for society going forward and suggests that further investigation should be done into how best to utilize new technologies like GPT-3 when tackling these challenges. Lastly, we conclude that while there is still much work left to do before any tangible effects can be seen from utilizing AI tools such as GPT-3 on societal mega-trends; early indications suggest it may have a positive impact if used correctly.

Research on urban heat mitigation has been growing in recent years with many of the studies focusing on green infrastructure (GI) as a strategy to mitigate the adverse effects of Urban Heat Island (UHI). This paper aims at presenting a review of the range of findings from GI research for urban heat mitigation through a review of scientific articles published during the years 2009-2019. This research includes a review of the different types of GI and its contribution for urban heat mitigation and human thermal comfort. In addition to analyzing different mitigation strategies, numerical simulation tools that are commonly used are also reviewed. It is seen that ENVI-met is one of the modelling tools that is considered as a reliable tool to simulate different mitigation strategies and hence has been widely used in the recent past. Considering its popularity in urban microclimate studies, this article also provides a review of ENVI-met simulation results that were reported in the reviewed papers. It was observed that the majority of the research was conducted on a limited spatial scale and focused on temperature and human thermal comfort.

Research on bidirectional human-machine interfaces will enable the smooth interaction with robotic platforms in contexts ranging from industry to tele-medicine and rescue. This paper introduces a bidirectional communication system to achieve multisensory telepresence during the gestural control of an industrial robotic arm. We complement the gesture-based control by means of a tactile-feedback strategy grounding on a spiking artificial neuron model. Force and motion from the robot are converted in neuromorphic haptic stimuli delivered on the user’s hand through a vibro-tactile glove. Untrained personnel participated in an experimental task benchmarking a pick-and-place operation. The robot end-effector was used to sequentially press six buttons, illuminated according to a random sequence, and comparing the tasks executed without and with tactile feedback. The results demonstrated the reliability of the hand tracking strategy developed for controlling the robotic arm, and the effectiveness of a neuronal spiking model for encoding hand displacement and exerted forces in order to promote a fluid embodiment of the haptic interface and control strategy. The main contribution of this paper is in presenting a robotic arm under gesture-based remote control with multisensory telepresence, demonstrating for the first time that a spiking haptic interface can be used to effectively deliver on the skin surface a sequence of stimuli emulating the neural code of the mechanoreceptors beneath.

The human virome is an area of increasing interest with relation to human health and disease. It has been demonstrated to alter in concert with the bacterial microbiome in early life and was also found to be different in patients with certain diseases such as inflammatory bowel disease. However, all virome analyses are hampered by a lack of annotated representative database sequences, often referred to as the ‘viral dark matter’. Here we provide the first description of the gut DNA virome in elderly individuals (>65 years old) as well as the description of novel bacteriophages not present in current reference databases. Diversity analysis comparing elderly persons from different residence locations (community living vs long term care facilities) did not reveal any difference in their virome diversity profiles despite the reported differences at the bacteriome level. An abundance of Microviridae of the subfamily Gokushovirinae were present in the faeces of elderly individuals. Several novel members of the order Caudovirales were also characterized and annotated. Assignment of host bacteria to detected viral genomes was attempted using a combination of CRISPR spacers, tRNA genes and a probabilistic approach. Further characterization of the viral dark matter is necessary for developing tools and expanding databases to study the human virome. This study focused on the virome of an aging human cohort with the goal of illuminating part of the viral dark matter.

The Release of government dataset for public use can potentially strengthen the relationship between the government and its constituents. However, research shows that there are several challenges for open data effectiveness. This paper reviews current determinants and issues associated with the open government data (OGD) procedures. The review concentrates on two ends of the spectrum: First, from the perspective of the preparation by the government, focusing on the organization of traditional governmental datasets and how the recording of the data is administered. Second, from the perspective of the users, focusing on the way in which the data is released to the general public and on human-computer interaction (HCI) issues between end-user and data-consumption interfaces. Following a thorough analysis of these two opposing challenges, the paper proposes approaches to mitigate them. This review and subsequent recommendations contribute and expand current understanding of open government data effectiveness and can lead to public policy changes, development of new procedures and strategies, and ultimately improvements at both ends of the federal open data endeavor.

The human body supports the growth of a wide array of microbial communities in various niches, such as the oral cavity, gastro-intestinal and urogenital tracts and on the surface of the skin. These host associated microbial communities include yet-un-cultivable bacteria and are influenced by various factors. Together, these communities of bacteria are referred to as the human microbiome. Human oral microbiome consists of both symbionts and pathobionts. Deviation from symbiosis among the bacterial community leads to “dysbiosis”—a state of community disturbance. Dysbiosis occurs due to many confounding factors that predispose to a shift in the composition and relative abundance of microbial communities. Dysbiotic communities have been a major cause for many microbiomes related systemic infections. Such dysbiosis is directed by certain important pathogens called the “keystone pathogens” that could modulate community microbiome variations. One such persistent infection is oral infection, mainly periodontitis, where a wide array of causal organisms has been implied to systemic infections such as cardio vascular disease, diabetes mellitus, rheumatoid arthritis and Alzheimer’s disease. The keystone pathogens co-occur with many yet-cultivable bacteria and their interactions lead to dysbiosis. This has been the focus of recent research. While immune evasion is one of the major modes that lead to dysbiosis, new processes and new virulence factors of bacteria have been shown to be involved in this important process of that determine disease or health state. This review focuses on such dysbiotic communities, their interactions and their virulence factors that predispose the host to other systemic implications.

Artificial Intelligence (AI) is used in our day-to-day life. AI for journalism is a reality. Just like other vital aspects of our life, AI has also entered the world of journalism. Many reputed news organizations have adopted AI in journalism to perform various newsroom tasks. In today’s digital world, several technologies are powering journalism. One such technology that is transforming the journalism field is artificial intelligence. Accuracy is a core value of journalism. With AI and machine learning systems, there’s a statistical element of uncertainty which means it’s impossible to guarantee 100 percent accuracy. AI tools such as OpenAI’s ChatGPT, Microsoft’s Bing chatbot, and Google’s Bard have been subject to debate now. AI tools have the potential to assist in a variety of tasks, such as scraping PDF files, writing code, and translating languages. AI can be a helpful tool for journalism students and media researchers. A main concern among computational journalists is that AI sometimes hallucinates data. This review paper highlights how artificial intelligence can help journalists.

Solid waste has become a tenacious issue worldwide. It has been increasing exponentially due to urbanization and the increase in the population. Since the twentieth-century technological revolution, there have been significant changes in the composition of solid waste. It poses significant challenges for waste management systems worldwide. Waste management is the process of handling waste right from its creation to its final disposal, including transport, collection, treatment, and monitoring. The present solid waste management (SWM) system is affected by unfavorable institutional, economic, technical, legislative, and operational constraints. Poor waste management is affecting ecosystems and human health, damaging our finite natural resources, impeding human economic progress, and harming people's quality of life. Now, researchers are concerned about the environment's degradation, a decline in quality of life, and risks related to waste management grow as the volume of solid waste. So, they are focusing on sustainable waste management practices which will be crucial for creating a cleaner and healthier environment for future generations. This chapter focuses on the concept of solid waste, its types, management, and its effect on the health of humans and the environment.

Some of the most promising small molecule toxins used to generate antibody drug conjugates (ADCs) include anti-mitotic agents (e.g auristatin and its derivatives) which are designed to attack cancerous cells at their most vulnerable state during mitosis. We were interested to identify a human cystostatic protein eventually showing comparable activities and allowing the generation of corresponding targeted fully human cytolytic fusion proteins. Recently, we identified the human microtubule associated protein tau (MAP tau) which binds specifically to tubulin and modulates the stability of microtubules thereby blocking mitosis and presumably vesicular transport. By binding and stabilizing polymerized microtubule filaments, MAP tau-based fusion proteins skew microtubule dynamics towards cell cycle arrest and apoptosis. This biological activity makes rapidly proliferating cells (e.g cancer and inflammatory cells) an excellent target for MAP tau-based targeted treatments. Their superior selectivity for proliferating cells confers additional selectivity towards upregulated tumor-associated antigens at their surface, thereby preventing off-target related toxicity against normal cells bearing tumor-associated antigens at physiologically normal to low levels. In this review, we highlight recent findings on MAP tau-based targeted cytolytic fusion proteins reported in preclinical immunotherapeutic studies.

Significant changes have occurred at Arabian Gulf Coast of Saudi Arabia over the last three decades. The area of mangrove was reduced by about 55%. Thus the economic, social, and environmental value of mangroves must be assessed over short -to long-term scales and employ these assessments for the awareness rising at local communities. This study provides a preliminary assessment of the risks on mangrove vegetation; it will provide database to mitigate the tremendous pressures due to coastal development and urban activities. The effects of human development on the mangrove plant cover in Eastern Region of KSA were recognised, during 2013 to 2016. The mean variations of physio-chemical characteristics in water and sediment were recorded. With regard to water analyses including; nitrogen, phosphorus, TSS, TDS, BOD, and turbidity were evaluated. On the other hand, for sediment: TDS, nitrogen, phosphorus, sulphate and total organic carbon were assessed. Moreover, the growth parameters: plant height, and size index, of Avicennia marina were recorded and estimated. It is concluded that human impact and urban developments have exerted drastic effects on the coastal ecosystems and its environments.

This paper presents a system developed for the assistance with a collaborative robot in hand-assisted laparoscopic surgery (HALS). The system includes a sensing glove with piezoresistive sensors which capture continuously the flexion degree of the surgeon's fingers. These data are analyzed using an algorithm that detects and recognize the selected movements. This information is sent as commands to the collaborative robot throughout the surgical operation. The bending patterns, speed and execution times of the movements are modelled in a pre-phase in which it will extract all the necessary information for later detection during the motion execution. The results obtained with 10 different volunteers show a high degree of accuracy and a low false discovery rate.

The oil and gas industry faces increasing pressure to remove people from dangerous offshore environments. Robots present a cost-effective and safe method for inspection, repair and maintenance of topside and marine offshore infrastructure. In this work, we introduce a new immobile multi-sensing robot, the Limpet, which is designed to be low-cost and highly manufacturable, and thus can be deployed in huge collectives for monitoring offshore platforms. The Limpet can be considered an instrument, where in abstract terms, an instrument is a device that transforms a physical variable of interest (measurand) into a form that is suitable for recording (measurement). The Limpet is designed to be part of the ORCA (Offshore Robotics for Certification of Assets) Hub System, which consists of the offshore assets and all the robots (UAVs, drones, mobile legged robots etc.) interacting with them. The Limpet comprises the sensing aspect of the ORCA Hub System. We integrated the Limpet with Robot Operating System (ROS), which allows it to interact with other robots in the ORCA Hub System. In this work, we demonstrate how the Limpet can be used to achieve real-time condition monitoring for offshore structures, by combining remote sensing with signal processing techniques. We show an example of this approach for monitoring offshore wind turbines. We demonstrate the use of four different communication systems (WiFi, serial, LoRa and optical communication) for the condition monitoring process. By processing the sensor data on-board, we reduce the information density of our transmissions, which allows us to substitute short-range high-bandwidth communication systems with low-bandwidth long-range communication systems. We train our classifier offline and transfer its parameters to the Limpet for online classification, where it makes an autonomous decision based on the condition of the monitored structure.

Exoskeleton robots are a rising technology in industrial contexts to assist humans in onerous applications. Mechanical and control design solutions are intensively investigated to achieve a high performance human-robot collaboration (e.g., transparency, ergonomics, safety, etc.). However, the most of the investigated solutions involve high-cost hardware, complex design solutions and standard actuation. In the presented work, an industrial exoskeleton for lifting and transportation of heavy parts is proposed. A low-cost mechanical design solution is proposed, exploiting compliant actuation at the shoulder joint to increase safety and transparency in human-robot cooperation. A hierarchic model-based controller is then proposed (including the modeling of the compliant actuator) to actively assist the human while executing the task. An inner optimal controller is proposed for trajectory tracking, while an outer fuzzy logic controller is proposed to online deform the task trajectory on the basis of the human’s intention of motion. A gain scheduler is also designed to calculate the optimal control gains on the basis of the performed trajectory. Simulations have been performed in order to validate the performance of the proposed device, showing promising results. The prototype is under realization.

Augment reality (AR) is crucial for immersive human-computer interaction (HCI) and vision of artificial intelligence (AI). Labeled data drove object recognition in AR. However, manual annotating data is expensive and labor-intensive, and furthermore, scanty labeled data limits the application of AR. Aiming at solving the problem of insufficient training data in AR object recognition, an automated vision data synthesis method called BAGAN is proposed in this paper based on the 3D modeling and GAN algorithm. Our approach has been validated to have better performance than other methods through image recognition task on natural image database ObjectNet3D. This study can shorten the algorithm development time of AR and expand the application scope of AR, which is of great significance to immersive interactive systems.

As a result of extensive research in the field of mobile robots (rovers) and swarms, a number of algorithms exist to assist them for executing a mission in the three levels of software architecture: strategic (interaction loop level), tactic (planning) and operational (sensing, control and actuation). They allow them to achieve their goals while adapting to their environment through a multitude of methods designed for each situation. For this reason, a literature review of the latest research conducted in previous years is required to identify new research trends in human-swarm interaction applied to help humans in hazardous environment such as militarized zone. In this paper, we will present some interesting algorithms for interactive and autonomous mobile robots acting in swarms in an open and crowded environment. A discussion will focus on comparing different algorithms and their advantages and disadvantages.

Recognition of the human interaction on the unconstrained videos taken from cameras and remote sensing platforms like a drone is a challenging problem. This study presents a method to resolve issues of motion blur, poor quality of videos, occlusions, the difference in body structure or size, and high computation or memory requirement. This study contributes to the improvement of recognition of human interaction during disasters such as an earthquake and flood utilizing drone videos for rescue and emergency management. We used Support Vector Machine (SVM) to classify the high-level and stationary features obtained from Convolutional Neural Network (CNN) in key-frames from videos. We extracted conceptual features by employing CNN to recognize objects from first and last images from a video. The proposed method demonstrated the context of a scene, which is significant in determining the behaviour of human in the videos. In this method, we do not require person detection, tracking, and many instances of images. The proposed method was tested for the University of Central Florida (UCF Sports Action), Olympic Sports videos. These videos were taken from the ground platform. Besides, camera drone video was captured from Southwest Jiaotong University (SWJTU) Sports Centre and incorporated to test the developed method in this study. This study accomplished an acceptable performance with an accuracy of 90.42%, which has indicated improvement of more than 4.92% as compared to the existing methods.

Wearable sensors for human physiological monitoring have attracted tremendous interest from researchers in recent years. However, most of the research was only done in simple trials without any significant analytical algorithms. This study provides a way of recognizing human motion by combining textile stretch sensors based on single-walled carbon nanotubes (SWCNTs) and spandex fabric (PET/SP) and machine learning algorithms in a realistic applications. In the study, the performance of the system will be evaluated by identification rate and accuracy of the motion standardized. This research aims to provide a realistic motion sensing wearable products without unnecessary heavy and uncomfortable electronic devices.

Pre-Western contact Hawaiʻi stands as a quintessential sustainability example of a large human population that practiced intensive agriculture, yet minimally displaced native habitats that comprised the foundation of its vitality. An explicit geospatial footprint of human-transformed areas across the pre-contact Hawaiian archipelago comprised less than 15% of total land area, yet provided 100% of human needs, supporting a thriving Polynesian society. A post-contact history of disruption of traditional Hawaiian land-use and its supplanting by Western land tenure and agriculture based on ranching, sugarcane, and pineapple, culminated in a landscape, in which over 50% of native habitats have been lost, while self-sufficiency has plummeted to 15% or less. Recapturing the ʻāina momona (productive lands) of ancient times can be accomplished through study of pre-contact agriculture, assessment of biological and ecological changes imposed on Hawaiian social-ecological systems, and conscious planned efforts to increase self-sufficiency and reduce importation. Impediments include the current tourism-based economy, competition from habitat-modifying introduced species, a suite of agricultural pests severely limiting traditional agriculture, and changes in climate rendering some pre-contact agricultural centers suboptimal. Modified agricultural methods will be required to counteract these limitations, and diversified agriculture to broaden the production base, without contributing to further degradation of native habitats.

This brief essay explores both the social and biological dimensions of human sexuality in light of the possibility of a substantial commerce in sexually-enabled robots, and concludes with some potentially strategic considerations for those who find themselves involved with their design, production, and/or marketing.

Cloud robotics is becoming an alternative to support advanced services of robots with low computing power as network technology advances. Recently, fog robotics has gained attention since the approach has merit relieving latency and security issues over the conventional cloud robotics. In this paper, a Function-as-a-Service based Fog Robotic (FaaS-FR) for cognitive robots is proposed. The model distributes the cognitive functions according to the computational power, latency and security with a public robot cloud and fog robot server. During the experiment with a Raspberry Pi as an edge, the proposed FaaS-FR model shows efficient and practical performance in the proper distribution of the computational work of the cognitive system.

Roaming complexity in terrains and unexpected environments poses significant difficulties in robotic exploration of an area. In a broader sense, robots have to face two common tasks during exploration namely walking on the drylands and swimming through the water. This research aims to design and develop an amphibious robot, which incorporates webbed duck feet design to walk on different terrains, swim in water and tackle obstructions on its way. The designed robot is compact and easy-to-use and also has the abilities to work autonomously. Such a mechanism is implemented by designing a novel robotic webbed foot consisting of two hinged plates. Due to the design, the webbed feet are able to open and close with the help of water pressure. The Klann linkage has been used to convert rotational motion to walking and swimming as an animal’s gait. Because of its amphibian nature, the designed robot can be used for exploring the tight caves, closed spaces and moving on uneven challenging terrains such as sand, mud or water. The presented model and design of the amphibious robot has been inspired by the working principle of duck feet. The propulsion generated through this feet system has been observed to be better and more controlled then the initial design and prototype of designed duck feet robot reported in [1]. The direction of movement can be managed, and the speed can be controlled using a larger contact area with water. Since duck is able to roam on both land and water, their feet movement during walking and swimming is adopted in designing the robot’s four legs. Klann linkage is used to mimic such duck feet motion and the control mechanism of the feet is driven by DC motors and DC servo motors which are governed by an Arduino microcontroller. The robot is capable of sensing the presence of water through conductive sensors and detects obstacles using ultrasonic sensor while walking. Due to its amphibian nature and other features in movement, the robot is capable of traversing diversified terrains. It is envisaged that the proposed design will be appreciated in the industry to design amphibious robots in near future.

Indigenous Australians have been particularly affected by type 2 diabetes mellitus due to their genetic susceptibility and a range of environmental risk factors. Recent genetic studies link predisposition to some diseases, including diabetes, to archaic humans, such as Neanderthals and Denisovans, suggesting persistence of ancient alleles in the genomes of modern humans. In this review we discuss the evolutionary role of the negative genetic selection associated with an adopted Western lifestyle as well as DNA variants influencing predisposition to obesity and diabetes in the Australian Indigenous population. We review the contribution of the ancient gene/pathways to the modern human phenotypes including the Neanderthal haplotype-tagging SNPs in NTRK2 gene, which may continue to play a role in obesity in Indigenous Australians.

The residential built form, including open space, provides the physical environment for social interaction. Understanding urban open space, including semi-public and public domains, through the lens of physical accessibility and visual permeability can potentially facilitate the building of a sense of community contributing to a better quality of life. Using an inner-city suburb in Perth, Western Australia as a case study, this research explores the importance of physical accessibility patterns and visual permeability for socialising in semi-public and public domains, such as the front yard and the residential streets. It argues that maintaining a balance between public and private inter-relationship in inner city residential neighbourhoods is important for creating and maintaining a sense of community.

Interviews from strength and conditioning coaches across all levels of athletic competition identified their two biggest concerns with the current state of wearable technology: (a) the lack of solutions that accurately capture data "from the ground up" and (b) the lack of trust due to inconsistent measurements. The purpose of this research is to investigate the use of liquid metal sensors, specifically Liquid Wire sensors, as a potential solution for accurately capturing ankle complex movements such as plantar flexion, dorsiflexion, inversion, and eversion. Sensor stretch linearity was validated using a Micro-Ohm Meter and a Wheatstone bridge circuit. Sensors made from different substrates were also tested and discovered to be linear at multiple temperatures. An ankle complex model and computing unit for measuring resistance values were developed to determine sensor output based on simulated plantar flexion movement. The sensors were found to have a significant relationship between the positional change and the resistance values for plantar flexion movement. The results of the study ultimately confirm the researchers' hypothesis that liquid metal sensors, and Liquid Wire sensors specifically, can serve as a mitigating substitute for inertial measurement unit (IMU) based solutions that attempt to capture specific joint angles and movements.

Estimation and planning play a vital role in the construction of an autonomous navigation framework. However, these problems are often considered separately, while planning gives robot a free-collision path towards the desired goal, estimation algorithm presents the executed trajectory in the sense that it has to be closed to the ground truth path as much as possible. Recently, a unified probabilistic framework, which supports solving these problems simultaneously, dubbed STEAP has been proposed. Nevertheless, its current version is only designated for an omni wheels robot, which allows robot to move and turn in vertical direction. Differential drive robot, on the other hand, though limited to move along only one direction, has been used in various situations due to its flexibility and lower cost in hardware designing. Thus, in this extension, our aim is to control a differential drive robot via STEAP. Moreover, in a more complicated environment such as labyrinth or maze, the original STEAP sometimes fails to find a path. Indeed, this problem is mainly caused by the poor initialization and the non-linearity in optimizer constraints. In our implementation, instead of dealing with these constraints, we employ a global planner algorithm such as Dijkstra or RRT to treat STEAP as an effective local planner module that focus on following the global path. Consequently, the experimental results show that the extended STEAP not only able to navigate a differential drive robot but also in a more complicated and unstructured environment.

This paper describes the successful application of the Transformer model used in the natural language processing and vision tasks as a means of processing the time series of signals from gyroscope and accelerometer sensors for the classification of human activities. The Transformer model is based on deep neural networks with many layers which can generalize well on signals. All measured signals come from a smartphone placed in a waist bag. Activity prediction is sequence-to-sequence, each time step of the signal is assigned a designation of the performed activity. Emphasis is placed on attention mechanisms, which express individual dependencies between signal values within a time series. In comparison with another recent result, the recognition precision was improved from 89.67 percent to 99.2 percent. The transformer model should in the future be included among the top options in machine learning methods for human activity recognition.

In The Work of the Dead: A Cultural History of Mortal Remains, Thomas Laqueur argues that the work of the dead is carried out through the living and through those who remember, honour, and mourn the dead. Further, he maintains that the brutal or careless disposal of the corpse “is an attack of extreme violence”. To treat the dead body as if it does not matter or as if it were ordinary organic matter would be to deny its humanity. From Laqueur’s point of view it is inferred that the dead are believed to have rights and dignities that are upheld through rituals, practices, and beliefs of the living. Drawing on dark tourism scholarship and cultural memory theory, this paper examines the display of human bones at Sedlec Ossuary, Czech Republic and the tourist culture that has built up around the site. Primarily, my writing calls into question the commoditization of burial places as a conceivable violation of the human rights of the dead. My research is driven by a number of questions: What is it that draws tourists to burial grounds and how do heritage sites negotiate visitor experiences? What are the ethical boundaries when a final resting place with bodies on display is also marketed as a tourist site? Do the dead have human rights and how are the living responsible for preserving those rights?

The objective is to study the local perceptions on the conservation of wild chimpanzees (Pan troglodytes verus) in the Réserve Naturelle Communautaire de Dindéfélo (RNCD), southeast of Senegal, to design specific actions to improve conservation management. We conducted 338 semi-structured interviews in three main villages of RNCD. Three-fourths of the population were farmers. Of those interviewed, 29% received elementary education. Two of the three villages participated in a project to plant trees as fences. On average, 66% of the respondents were animists. Of the respondents who were afraid to see a chimpanzee, 68% answered because they attack. Seventy-seven percent washed their clothes in the forest river because there was more water than in the village wells. Of the interviewees who threw the old clothes into the forest, 50% did it due to tradition. Ninety-six percent of respondents stated that chimpanzees do not feed from their crops. The main problems of the locals with the Reserve were lack of water and basic resources and not been allow to cut trees in protected areas. There were significant relationships between education (1 relationship), environmental project (4 relationships) and animism (11 relationships) with local perceptions. The 93% of the respondents who had the traditional belief that “if the old clothes were burnt, children would become sick” feel fear of chimpanzees, while those who did not have this animistic belief the 6% are afraid (χ² = 1.57, P < 0.02). These local perceptions allow us to design specific course of action to improve chimpanzee conservation and sustainable coexistence in this complex period of the Anthropocene.

We have successfully measured amyloid beta (Aβ) (1-40) protein added in human serum by a NiCr strain gauge cantilever biosensor immobilized with liposomes incorporating cholesterol. Importantly, we investigated the effect of incorporation of cholesterol in the liposome in order to suppress the interaction between the liposome and many different proteins included in human serum. It was revealed that incorporating cholesterol suppresses the interaction between the proteins other than Aβ in human serum and the liposome. Finally, we detected Aβ(1-40) in human serum with typical chronological behaviors due to Aβ aggregation and fibrillization. Furthermore, as a digital low-pass filtering procedure could reduce external noises, the cantilever sensor immobilized with liposome incorporating cholesterol can detect low-concentrated Aβ in human serum.

Organophosphate pesticides (also known OPPs) have for many years been the choice candidate globally for pest control. OPPs have for over 80 years, been used in gardens, fields and greenhouses as crop protection agents, and even in homes as insecticides and mosquito abatement. Thus, year after year and with the repeated application of organophosphate agents, many problems have appeared as a result of excessive use of pesticides. The adverse effects of pesticides are well documented in human health, environment, pesticide residue in crops, soil and water contaminated by these pesticides. Therefore, it is necessary to shed more light on the risks associated with the irresponsible usage of organophosphate pesticides.

Solar Energy has enormous potential to fulfill the energy requirements of the world and can be extracted using solar cells. However, the solar cells are affected by poor efficiency and further affected by wind speed, orientation of the panel, temperature and dust deposition. There are different cleaning technologies devised by many industry experts to clean the solar panels. However, they are facing drawbacks when deployed in the solar farm. An efficient cleaning system, along with an added cooling system must be devised, so that the solar panels must be cleaned and cooled often to maximize the energy production. This paper presents a low-cost, energy-efficient, smart and innovative dust cleaning and cooling system for Photovoltaic (PV) Panels. The system is designed, fabricated, fully-automated using Programmable Logic Controller (PLC) and tested successfully. A battery-charging kiosk, capable of charging two, 24V Lead Acid Batteries embedded within this prototype, shall provide clean-energy in a sustainable manner to the rural communities of the developing nations. The user can check the status of the battery such as battery voltage, battery temperature, and state of charge on the Human Machine Interface (HMI) panel while charging the batteries

Duchenne muscular dystrophy (DMD) is a fatal X-linked recessive neuromuscular disease prevalent in 1 in 3500 to 5000 males worldwide. As a result of mutations that interrupt the reading frame of the dystrophin gene (DMD), DMD is characterized by a loss of dystrophin protein which leads to decreased muscle membrane integrity, which increases susceptibility to degeneration. CRISPR/Cas9 technology has garnered interest as an avenue for DMD therapy due to its potential for permanent exon skipping, which can restore the disrupted DMD reading frame in DMD and lead to dystrophin restoration. An RNA-guided DNA endonuclease system, CRISPR/Cas9 allows for the targeted editing of specific sequences in the genome. The efficacy and safety of CRISPR/Cas9 as a therapy for DMD has been evaluated by numerous studies in vitro and in vivo, with varying rates of success. Despite the potential of CRISPR/Cas9-mediated gene editing for the long-term treatment of DMD, its translation into the clinic is currently challenged by issues such as off-targeting, immune response activation, and sub-optimal in vivo delivery. Its nature as being mostly a personalized form of therapy also limits applicability to DMD patients, who exhibit a wide spectrum of mutations. This review summarizes the various CRISPR/Cas9 strategies that have been tested in vitro and in vivo for the treatment of DMD. Perspectives on the approach will be provided, and the challenges faced by CRISPR/Cas9 in its road to the clinic will be briefly discussed.

Purpose: The ongoing COVID-19 crisis has created the need for social distancing between medical staff and patients, therefore, healthcare robotics is increasingly important as regards infectious control. The purpose of this study was to design and fabricate a remote controlled transport robot that could provide an optimum technology base for developing countries, having dual functions of telepresence and delivery operations inside or outside buildings, is easy to assemble and also easily able to be cleaned using standard chemical agents.Methods: This machine was designed and fabricated to incorporate two main functions; delivery and telepresence. The robot consists of a base frame, DC motors for driving and steering systems, actuator motor for driving wheel elevation, Wi-Fi AP/Repeater, network camera, and a necessary a back-up power system.Results: This robot can be used both indoors and outdoors. It can carry loads of more than 80 kg. The machine can cross obstacles greater than 3 cm. It can be cleaned by disinfectant agents. An actuator motor can lift the driving wheels above the surface to prevent gear damage. Conclusion: This article demonstrates the concept of a healthcare telepresence and delivery robot that can provide distancing during the present COVID-19 crisis, using optimum technology for low and middle-income countries. In the future, the researcher should consider development of the delivery controls, combined with the telepresence system using the internet of things, to improve the navigation system, the manipulator system for supporting multi-floor transportation, and also other multi-functional purposes.

Excessive vibration of temporary grandstand owing to crowd has lateral rhythmic motions can cause human in panic, which attracted increasing attention in recent years. This paper focuses on experiments that a temporary grandstand occupied 20 participants is oscillated by a shaking table with a series of random waves and induced by crowd with rhythmic swaying motions at lateral direction, respectively. And then a series of vibration perception questionnaires about crowd reaction are obtained. Evaluation of annoying level derives in concept of degree of membership and annoying rate method is proposed, then the human health risk of vibration serviceability of temporary grandstand is assessed with acceleration vibration dose value(VDV). From these results it is clear that standing crowd is more tolerant to vibration than seated crowd. The measured vibrations generated by crowd activities on temporary grandstand can cause panic in crowd. New relationship between the annoyance rate and structural acceleration VDV at logarithmic coordinate is proposed. The findings of this study can be utilized to manage the vibration of temporary grandstand and assess the human health risk.

Due to their unique properties, perfluorinated substances (PFAS) are widely used in multiple industrial and commercial applications, but they are toxic for animals, humans included. This review presents some available data on the PFAS environmental distribution in the world, and in particular in Europe and in Veneto region of Italy, where it has become a serious problem for human health. The consumption of contaminated food and drinking water is considered one of the major source of exposure for humans. Worldwide epidemiological studies report the negative effects that PFAS have on human health, due to environmental pollution, including infertility, steroid hormone perturbation, thyroid, liver and kidney disorders and metabolic disfunctions. In vitro and in vivo researches correlated PFAS exposure to oxidative stress effects (in mammals as well as in other vertebrates of human interest), produced by a PFAS-induced increase of reactive oxygen species formation. The cellular antioxidant defense system is activated by PFAS, but it is only partially able to avoid the oxidative damage to biomolecules.

The increasing number of robots around us will soon create a demand for connecting these robots in order to achieve goal-driven teamwork in heterogeneous multi-robot systems. In this paper, we focus on robot teamwork specifically in dynamic environments. While the conceptual modeling of multi-agent teamwork has been studied extensively during the last two decades, related engineering concerns have not received the same degree of attention. Therefore, this paper makes two contributions. The analysis part discusses general design challenges that apply to robot teamwork in dynamic application domains. The constructive part presents a review of existing engineering approaches for challenges that arise with dynamically changing runtime conditions. An exhaustive survey of robot teamwork aspects would be beyond the scope of this paper. Instead, we aim at creating awareness for the manifold dimensions of the design space and highlight state-of-the-art technical solutions for dynamically adaptive teamwork, thus pointing at open research questions that need to be tackled in future work.

The human capital index is a multivariate variable used to measure the assessment of human resource productivity in the future. Human capital has a connection with economic development. Moreover, the study looks to analyses the relationship between Human Capital and Economic Growth indicators such as HCI and GDP. Human Capital relates to the development of individual skills to make them function more profitable for the national economy. The study was aimed at investigating the effect of population, the number of people engaged and the average annual hours worked by engaged persons on the human capital index. The analysis was in SPSS for the findings. Correlation tests were used for analysis. The study revealed that population, the number of people engaged and the average annual hours worked by engaged persons had a significant positive effect on the human capital index.

Human African Trypanosomiasis (HAT) commonly known as sleeping sickness, is a neglected tropical vector borne disease caused by trypanosome protozoa. It is transmitted by bites of infected tsetse fly. In this paper we first present the vector-host model which describes the general transmission dynamics of HAT. In the tsetse fly population, the HAT is modelled by three compartments while in the human population, the HAT is modelled by four compartments. The next generation matrix approach is used to derive the basic reproduction number, R₀, and also it is proved that if R₀ ≤ 1 the disease free equilibrium is globally asymptotically stable, which means the disease dies out. The disease persist in the population if the value of R₀ > 1. Furthermore, the optimal control model is determined by using the Pontryagin’s maximum principle with control measures such as education, treatment and insecticides used to optimize the objective function. The model simulations confirm that the use of the three control measures are very efficient and effective to eliminate HAT in Africa.

The paper presents an innovative hexapod walking robot built with 3-UPU parallel mechanism. In the robot, the parallel mechanism is used as both an actuator to generate walking and also a connecting body to connect two groups of three legs, thus enables the robot to walk with simple gait by very few motors. In the paper, the forward and inverse kinematics solutions are obtained. The workspace of the parallel mechanism is analyzed using limit boundary search method. The walking stability of the robot is analyzed, which yields the robot’s maximum step length. The gait planning of the hexapod walking robot is studied for walking on both flat and uneven terrains. The new robot, combining the advantages of parallel robot and walking robot, has a large carrying capacity, strong passing ability, flexible turning ability, and simple gait control for its deployment for uneven terrain.

The knowledge of human body proportion and segmental properties of limbs, head and trunk are of fundamental importance in biomechanical research. As many methods are employed, it is important to know which they are currently available, which data on human body masses, lengths, center of mass (COM) location, weights and moment of inertia are available and which methods are most suitable for a specific research purposes. The present review examines the literature concerning human body segments properties for biomechanical purposes. It emerges that data obtained in studies on cadaveric specimens are still the most accurate, whilst technological tools currently available are manifolds, each one with proper advantages and disadvantages. Classical studies were focused mainly on white men, while in recent year the available data of body segments has been extended to children, woman, and other races. Also, data on special population (obese, pregnant women) are starting to appear in the scientific literature.

In Italy HPV vaccination was implemented for girls since 2007 but its coverage was lower than recommended level. Sicily is one of the Italian administrative regions with lower vaccination coverage, ranging in the birth cohorts 1996–1999 from 59% to 62%. Aim of the study was to investigate factors associated with refusal of anti-HPV vaccination among young adult women of Palermo, Italy. A cross-sectional study was conducted through the administration of a telephone questionnaire, consisting of 23 items on HPV infection and vaccination knowledge based on Health Belief Model framework. The eligible population were young women with at least a previous vaccination among all included in Sicilian Vaccination schedule, without starting or completing anti-HPV vaccination schedule. Overall, 141 young women were enrolled, of them 84.4% were unvaccinated and 15.6% had at least one dose of HPV vaccine. In multivariate analysis, factors associated with the failure to perform the HPV vaccination were degree as school level (OR = 10.2, p = 0.041), lower participation at school seminar on HPV (OR = 0.2, p = 0.047) and lower perception of anti-HPV vaccine benefits (OR = 0.4, p = 0.048). Public health educational program focusing and tailored on benefits perception of anti-HPV vaccine and HPV disease severity, especially if carried out at school, can improve HPV vaccination uptake.

Abstract: Mass spectrometry research laboratories reported multiple probes for ambient ionization in the last years. Combining them with a mechanical moving stage enables automated sampling and imaging applications. We developed a robotic platform, which is based on RepRap 3D-printer components, and therefore easy to reproduce and to adopt for custom prototypes. The minimal step width of the Open LabBot is 12.5 μm, and the sampling dimensions (x, y, z) are 18 × 15 × 20 cm. Adjustable rails in an aluminium frame construction facilitate the mounting of additional parts such as sensors, probes, or optical components. The Open LabBot uses industry-standard G-code for its control. The simple syntax facilitates the programming of the movement. We developed two programs: 1) LABI-Imaging, for direct control via a USB connection and the synchronization with MS data acquisition. 2) RmsiGUI, which integrates all steps of mass spectrometry imaging: The creation of G-code for robot control, the assembly of imzML files from raw data and the analysis of imzML files. We proved the functionality of the system by the automated sampling and classification of essential oils with a PlasmaChip probe. Further, we performed an ambient ionization mass spectrometry imaging (AIMSI) experiment of a lime slice with laser desorption low-temperature plasma (LD-LTP) ionization, demonstrating the integration of the complete workflow in RmsiGUI. The design of the Open LabBot and the software are released under open licenses to promote their use and adoption in the instrument developers’ community.

Psoriasis is a chronic immune-mediated skin disease that involves interaction of both immune and skin cells, and is characterized by cytokine-driven epidermal hyperplasia, deviant differentiation, inflammation and angiogenesis. Because available treatments for psoriasis have significant limitations, dietary products are potential natural sources of therapeutic molecules, which can rescind molecular defects associated with psoriasis and could be developed for its management. Fisetin (3,7,3′,4′- tetrahydroxyflavone), a phytochemical naturally found in pigmented fruits and vegetables has demonstrated pro-apoptotic and antioxidant effects in several malignancies. This study utilized biochemical, cellular, pharmacological and tissue-engineering tools to characterize the effects of fisetin on normal human epidermal keratinocytes (NHEKs), peripheral blood mononuclear cells (PBMC) and CD4+ T lymphocytes in 2D and 3D psoriasis-like disease models. Fisetin treatment of NHEKs dose and time-dependently induced differentiation and inhibited interleukin-22-induced proliferation, as well as activation of the PI3K/Akt/mTOR pathway. Fisetin treatment of TNF-α-stimulated NHEKs significantly inhibited the activation of p38 and JNK, but had no effect on ERK1/2. In addition, fisetin treatment significantly decreased the secretion of Th1/Th-17 pro-inflammatory cytokines, particularly IFNγ and IL-17A by 12-O- tetradecanolylphorbol 13-acetate (TPA)-stimulated NHEKs and anti-CD3/CD28-activated human PBMCs. Furthermore, we established the in-vivo relevance of fisetin functions, using a 3D full-thickness human skin model of psoriasis (FTRHSP) that closely mimics in-vivo human psoriatic skin-lesions. Herein, fisetin significantly ameliorated psoriasis-like disease features, and decreased the production of IL-17 by CD4+ T lymphocytes co-cultured with FTRHSP. Collectively, our data identify pro-differentiative, anti-proliferative and anti-inflammatory effects of fisetin, via modulation of PI3K-Akt-mTOR and p38/JNK pathways and the production of cytokines in 2D and 3D human skin model of psoriasis. These results suggest that fisetin has a great potential to be developed as an effective and inexpensive agent for the treatment of psoriasis and other related inflammatory skin disorders.

AGI could arise within the next decades, promising a decisive strategic advantage. This paper discusses risks, associated with the development of AGI: destabilizing effects on strategic balance, underestimating risks in the interest of victory in the race, egoistically exploiting the huge benefits by a tiny minority. Further: Developed AGI could be beyond human control. Human goals could not be implemented and an intelligence explosion to superintelligence could take place leading to a total loss of control and power. If competition for AGI is non-transparent, secret, uncontrolled and not regulated, it’s possible that risks could not be managed and would lead to catastrophic consequences. The danger corresponds to that of nuclear weapons. It is crucial that the key actors of a possible AI Race agree at an early stage on the prevention and transparent regulation of a possible AI Race - similar to measures to secure strategic stability, on arms control measures, disarmament, and prevention of the proliferation of nuclear weapons. The realization that an uncontrolled AI race can lead to the extinction of humanity - this time even independent of human will – requires analogous measures to contain, prevent, regulate and secure an AI race within the framework of AGI development.

Open-plan office (OPO) layouts emerged to allow organizations to adapt to changing workplace demands. We explore the potential for OPOs to provide such adaptive capacity to respond to two contemporary issues for organizations: the chronic challenge of environmental sustainability, and the acute challenges emerging from the great COVID-19 homeworking experiment. We apply a socio-technical systems perspective and green ergonomics principles to investigate the relationship between an OPO environment and the occupants working within it. In doing so, we consider the relevant technical and human factors such as green technology and employee green behavior. We also consider how a green OPO might provide non-carbon benefits such as improving occupant wellbeing and supporting the emergence of a green organizational culture. Our investigation highlights several avenues through which an OPO designed with green ergonomic principles could benefit occupants, the organizations they work for, and the natural environment of which they are a part and on which they depend. We find reason to suspect that green OPOs could play an important role in sustainable development; and offer a research agenda to help determine whether in fact OPOs are another example of how going green is good business.

In this paper we review dynamics and roughness of bonds in proteins on example of albumin, that is important from the physiological point of view. We have performed computer simulations of albumin chain. Statistics were collected by performing many simulations realizations for each experimental setting. We concentrate on hydrogen bonds, cation-π and π- π interactions and NP contacts. Histograms of hydrogen bonds length are positively skewed in contrary to histograms of interactions and HP contacts that are negatively skewed. Scaling exponents of power spectra of energies of bonds / interactions /contacts are in range -0.2 to -0.5 and significantly differ between various hydrogen bonds or interactions. Varying scaling of such spectra can be used to classify between distinct bonds or contacts. Concerning particular amino-acids, largest amount of HBO H20 bonds are between Glutamate (GLU) amino-acids and water particle, while large amount of HBO bonds are formed with Lysine (LYS). For HP contacts the mayor role plays Phenylalanine (PHE) and Leucine (LEU) amino-acids. From decay curves HBO H2O bonds decays in fastest rate, while HBO bonds and HP contacts at slowest rate. We present as well decay curves of bonds formed by particular amino-acids, that gives interesting results.

Deforestation and associated changing landscapes are major components of environmental changes, with important implications for ecosystem functioning and biodiversity conservation. Tropical forests are hot spots of biodiversity and provide multiple goods and ecosystem services which benefit people in many ways Forest also play an important role in health-related legends, myths, and fairy tales from all over the world, and are important sources of new potential emerging microbial threats to human. Although plausibly numerous abundant microbial forms with a forest origin may exist, our systematic literature review shows that forest-derived infection studies are relatively unexplored, and both taxonomically and geographically biased. Since biodiversity has been associated with emergence of novel infectious diseases at macro-scale, we describe the main biogeographical patterns in the emerging infection-biodiversity-forest loss nexus. Then, we illustrate four fine-scale case studies to decipher the underlying processes of increased infection risk in changing forest clearing landscapes. Finally, we identify scientific challenges and regional management measures required to mitigate these important new emerging threats.

The ongoing COVID-19 global pandemic is affecting every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and citywide implemented lockdown measures are affecting virus transmission, people’s travel patterns, and air quality. Many studies have been conducted to predict the COVID-19 diffusion, assess the impacts of the pandemic on human mobility and air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This review study aims to analyze results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people's socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel purposes to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths of the people. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also discusses policy implications, which will be helpful for policymakers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.

Epstein–Barr virus (EBV) has been recently shown to be co-present with high-risk human papillomaviruses (HPVs) in human cervical cancer; thus, these oncoviruses play an important role in the initiation and/or progression of this cancer. Accordingly, our group has recently viewed the presence and genotyping distribution of high-risk HPVs in cervical cancer in Syrian women; our data pointed out that HPVs are present in 95.45% of our samples. Herein, we aim to explore the co-prevalence of EBV and high-risk HPVs in 44 cervical cancer tissues from Syrian women using polymerase chain reaction (PCR), immunohistochemistry (IHC) and tissue microarray (TMA) analyses. We found that EBV and high-risk HPVs are co-present in 15/44 (34%) of the samples. Additionally, we report that the co-expression of LMP1 and E6 genes of EBV and high-risk HPVs, respectively, is associated with poorly differentiated squamous cell carcinomas phenotype; this is accompanied by a strong and diffused Id-1 overexpression, which is an important regulator of cell invasion and metastasis. These data imply that EBV and HPVs are co-present in cervical cancer in the Middle East area including Syria and their co-presence is associated with a more aggressive cancer phenotype. Future investigations are needed to elucidate the exact role of EBV and HPVs cooperation in cervical carcinogenesis.

Background and Research Highlights: Despite all the concerns and initiatives, natural resources like forests, as well as biodiversity are decreasing at an alarming rate worldwide. Conservation is considered as one of the major tools to prevent such loss and rapid degradation. Evidence around the world shows the adverse effects of conservation laws and policies on indigenous peoples and other local communities. Objectives: This study was conducted in one of the forest-dependent communities situated in Sundarban (world’s largest mangrove forest) to understand the impact of conservation laws and policies on their livelihood. Materials and Methods: A qualitative methodology was designed to collect data, using focus group discussions and case study with community people, and individual interviews with the personnel from NGOs and relevant government departments. Findings: Strict conservation policies and restrictions in accessing forest resources made lives and livelihoods of the local community insecure and unstable, thus putting the community in a vulnerable situation. The had to leave their traditional mode of income and look for alternative livelihood options. Almost no evidence was found in relation to upkeeping their rights in conservation activities. Prohibited movement, provision of punishment for entering into the forest without proper permission and struggles in everyday life were some of the highlighted issues. They had no participation in conservation activities, management of alternative livelihood options, and even they were not sensitized before putting restrictions. Although they had a history of emotional and physical attachment with the forest, existing activities did not consider these issues. In addition, corruption and abuse of power by law enforcement agencies towards the local community intensified the sufferings. Conclusion: This study argues that the realization of human rights in conservation activities and the sensitization of the implementing stakeholders are prerequisites for ensuring the sustainability of both biodiversity and the affected people.

Emergence of more virulent forms of human pathogenic bacteria with multi drug resistance is a serious global issue and requires alternative control strategies. The current study was focused to investigate the antibacterial and antibiofilm potential of ferulic acid grafted chitosan (CFA) against Listeria monocytogenes (LM), Pseudomonas aeruginosa (PA), and Staphylococcus aureus (SA). The present result showed that CFA at 64 µg/mL concentration exhibit bactericidal action against LM and SA (>4 log reduction) and bacteriostatic action against PA (<2 log CFU) within 24 h of incubation. Further studies based on propidium iodide uptake assay, measurement of material released from the cell, and electron microscopic analysis revealed that the bactericidal action of CFA was due to the altered membrane integrity and permeability. CFA dose-dependently inhibited biofilm formation (52-89% range), its metabolic activity (30.8-75.1% range) and eradicated mature biofilms, and reduced viability (71-82% range) of the test bacteria. Also, the swarming motility of LM was differentially affected at sub-MIC concentration of CFA. In the present study, the ability of CFA to kill and alter the virulence production in human pathogenic bacteria will insight a new scope for the application of these biomaterials in healthcare to effectively treat bacterial infections.

Aim: To develop an index for quantitative assessment of the upper limb motor function in children with cerebral palsy before and after robot-assisted therapy. Method: An upper limb motor function index was developed using kinematic, surface electromyography and three-axis inertial measurements unit data collected from 15 children with cerebral palsy (CP) and 15 typically developed children. Children with CP underwent 18 robot-assisted therapy sessions with the REAplan device. All children were evaluated, using kinematic data from the REAplan, electromyography and three-axis inertial measurements unit readings from its accelerometer. A principal component analysis was conducted to produce an evaluation index, which is able to detect the deviation from the upper limb motor function of typically developing children group. Children with CP were evaluated twice before and after the intervention with Box and Blocks test and Finger-To-Nose test. The discriminative and concurrent validity of the upper limb motor function index were investigated. Results: The upper limb motor function index was higher in children with CP post therapy (p<0.001). Finger-To-Nose test values improved after robot-assisted therapy (p<0.03). A weak but positive correlation was observed between upper limb motor function index and clinical tests (r=0.012, p=0.95 and r=0.13, p= 0.54 for Box and Blocks test and Finger-To-Nose test respectively). Interpretation: The upper limb motor function index successfully differentiated between the typically developing children and children with CP and was effective in assessing the improvement of the upper limb motor function after robot-assisted therapy. The upper limb motor function index could be extended to assess and monitor rehabilitation therapies of other populations, such as those with stroke and Parkinson’s disease.

Wire-driven hyper-redundant continuum manipulators are gaining more popularity and finding more applications in industry and in minimally invasive surgery. Unlike traditional rigid link manipulators, continuum robots with a flexible backbone structure are able to work in a highly constrained workspace and in an unstructured environment. However, in spite of a possible wide range of reachability, continuum manipulators have some issues related to payload capacity, accuracy and control. Therefore, in this research, we propose a novel hyper-redundant continuum robot with a passive sliding disc mechanism to improve payload capacity and accuracy. To prove the sliding mechanism concept, we demonstrate a comparison analysis with a conventional non-sliding continuum robot arm in a payload test, a bending test and a reachability test. Moreover, with this novel design, we are proposing robot kinematics and kinetic formulation and simulation results to validate the effectiveness of the sliding disc mechanism.

Coronavirus that is also known as COVID-19 disease is produced by SARSCoV-2. This causative agent is highly contagious and can cause potentially fatal pneumonia worldwide with serious public health concerns. In the beginning among infected individuals, most of them were those who were mainly shown to the wet animal market in a big city of China known as Wuhan. So, it was suggested that this was almost certainly the zoonotic source of COVID-19illness. The transitional source of origin and their mode of transmission to humans were not known obviously. Conversely, from human to human rapidly transformation have been confirmed generally. Currently, there is no availability of FDA approved clinically antiviral drugs and/or vaccines to be used against the COVID-19. Afterward, SARS-CoV and MERS-CoV, the occurrence of SARS-CoV-2 has been manifested as the third sketch of an enormously pathogenic coronavirus into human population globally. In this review, we provide a brief overview of the history of COVID-19 in Pakistan up-to 18^th weeks after beginning, current situation, epidemiology, and its impact on the human population. Moreover, we focused on physiological variation during the incubation period, genome analysis of SARS-CoV2, supportive treatment approaches, and safety measures in the Pakistani population, which may be supportive for combating the risk of COVID-19 epidemic. We also reviewed the future approaches for the development of therapeutic interventions and vaccines to cope with the COVID-19 epidemic.

Recent advances in recording and real-time analysis of surface electromyographic signals (sEMG) have fostered the use of sEMG human-machine interfaces for controlling personal computers, prostheses of upper limbs, and exoskeletons among others. Despite a relatively high mean performance, sEMG-interfaces still exhibit strong variance in the fidelity of gesture recognition among different users. Here we systematically study the latent factors determining the performance of sEMG-interfaces in synthetic tests and in an arcade game. We show that the degree of muscle cooperation and the amount of the body fatty tissue are the decisive factors in synthetic tests. Our data suggest that these factors can only be adjusted by a long-term training, which promotes fine-tuning of low-level neural circuits driving the muscles. A short-term training has no effect on synthetic tests, but significantly increases the game scoring. This implies that it works at a higher decision-making level, not relevant for synthetic gestures. We propose a procedure that enables quantification of the gestures’ fidelity in a dynamic gaming environment. For each individual subject the approach allows identifying “problematic” gestures that decrease gaming performance. This information can be used for optimizing the training strategy and for adapting the signal processing algorithms to individual users, which could be a way for a qualitative leap in the development of future sEMG-interfaces.

The increasing number of registered road crashes involving cyclists during the last decade, and the high proportion of road crashes resulting in severe injuries and fatalities among cyclists constitutes a global issue for community health, urban development, and sustainability. Nowadays, the incidence of many risk factors for road crashes of cyclists remains largely unexplained. Given the importance of this issue, the present study has been conducted with the aim of determining relationships between infrastructural, human factors and safety outcomes of cyclists. Objectives: This study aimed, first, to examine the relationship between key infrastructural and human factors present in cycling, bicycle-user characteristics, and their self-reported experience with road crashes. And second, to determine whether a set of key infrastructural and human factors may predict their self-reported road crashes. Methods: For this cross-sectional study, a total of 1064 cyclists (38.8% women, 61.2% men; M = 32.8 years of age) from 20 different countries across Europe, South America and North America, participated in an online survey composed of four sections: demographic data and cycling-related factors, human factors, perceptions on infrastructural factors, and road crashes suffered. Results: The results of this study showed significant associations between human factors, infrastructural conditions and self-reported road crashes. Also, a logistic regression model found that self-reported road crashes of cyclists could be predicted through variables such as age, riding intensity, risky behaviors, and problematic user/infrastructure interactions. Conclusions: The results of this study suggest that self-reported road crashes of cyclists are influenced by features related to the user and their interaction with infrastructural characteristics of the road.

Humans are exposed to the environmental pollutants 2, 4-dichlorophenol (2, 4-DCP) and 2, 5-dichlorophenol (2, 5-DCP) through air, use of water and consuming products. In this study, we evaluated the urinary concentrations of these compounds in Korean people from the age of 18 to 69 years by making use of the data from the Korean National Human Biomonitoring Survey that was completed in 2009. Of 1,865 representative Koreans, 63.4% and 97.9% were found to have concentrations of 2, 4-DCP and 2, 5-DCP > 0.05 μg/L (limit of detection) in their urine. The geometric mean urinary concentrations were 0.14 μg/L (confidence interval of 95% = 0.13–0.16) and 0.44 μg/L (confidence interval = 0.41–0.48), respectively. It was fond that the adjusted proportional changes in 2, 4-DCP concentrations were significantly associated with body mass index, whereas those of 2, 5-DCP concentrations were influenced by place of residence. From these findings, it is evident that most adults in Korea have levels of 2, 4-DCP and 2, 5-DCP that are detectable in their urine and burden of these compounds on their bodies varies depending on numerous demographic factors.

This study investigated the capability of vocal-identity-cloning Artificial Intelligence (AI) to encode human-specific confident, doubtful, and neutral-intending emotive states. Linear mixed-effects models and machine learning classification with eXtreme Gradient Boosting were employed to examine the underlying acoustic signatures from 2,700 audio clips, comprising of sentences spoken by human speakers and two sets of equivalences (AI-Geography/AI-Trivia, based on the trained text) generated by voice-cloning models designed to clone human speakers’ identities. Compared with neutral-intending voice, human speakers lengthened their vocal tract, raised the fundamental frequency, and increased Chroma constant-Q transform when they intended to be confident; An opposite pattern was shown when they intended to be doubtful. The two sets of AI sounds displayed a similar pattern to human speech, suggesting a shared mechanism for encoding vocal expression across sources. The 1,000 times training-testing classification models reported an in-group advantage for AI sources. The algorithms, trained on AI-Geography/AI-Trivia, resulted in higher accuracies when tested within these AI sources than when tested on human audio. All between-source classifications reported above-chance-level (1/3) accuracies. These findings highlighted that voice-cloning AI, the widely used conversational agent, can learn and generate human-specific vocally-expressed confidence.

Ancient DNA has given a new vision to the recent history of human evolution. However, by always relying on the information provided by whole genome sequencing, some relevant relationships between modern humans and its archaic relatives have been misinterpreted by hybridization and recombination causes. In contrast, the congruent phylogeny, obtained from non-recombinant uniparental markers, indicates that humans and Neanderthals are sister subspecies, and that the most recent common ancestor of modern humans was not of African origin but Eurasian.

Bartonella spp. bacteria can be found around the globe and are the causative agents of multiple human diseases. The most well-known infection is called cat-scratch disease, which causes mild lymphadenopathy and fever. As our knowledge of these bacteria grows, new presentations of disease have been recognized with serious manifestations. Not only has more severe disease been associated with these bacteria, but Bartonella species have also been discovered in a wide range of mammals and the pathogens’ DNA can be found in multiple vectors. This review will focus on some common mammalian reservoirs as well as the suspected vectors in relation to disease transmission and prevalence. Understanding the complex interactions between these bacteria, their vectors, and reservoirs as well as the breadth of infection by Bartonella around the world will help toassess the impact of Bartonellosis on public health.

Mounting evidence supports the connections between exposure to environment types––such as green spaces and biodiversity––and human health. However, the mechanistic links that connect biodiversity (the variety of life) and human health, plus the level of supporting evidence, are less clear. Here, we undertook a scoping review to map the links between biodiversity and human health and summarise the levels of associated evidence using an established weight of evidence framework. Distinct from other reviews, we provide additional context regarding the environment-microbiome-health axis, evaluate the environmental buffering pathway (e.g., biodiversity impacts on air pollution), and draw upon expert opinion to provide case studies on three underrepresented linkages. The case studies include (1) biodiversity and Indigenous Peoples’ health, (2) biodiversity and urban social equity, and (3) biodiversity and COVID-19. We observed a moderate level of evidence to support the environmental microbiota-human health pathway and a moderate-high level of evidence to support broader nature pathways (e.g., green space) to various health outcomes, from stress reduction to enhanced wellbeing and improved social cohesion. However, studies of broader nature pathways did not typically include specific biodiversity metrics, indicating clear research gaps. Further research is required to understand the connections and causative pathways between biodiversity (e.g., using metrics such as taxonomy, diversity/richness, structure, and function) and health outcomes. There are well-established frameworks to assess the effects of broad classifications of nature on human health. These can assist future research in linking biodiversity metrics to human health outcomes. Our case studies on underrepresented linkages highlight the roles of biodiversity and its loss on urban lived experiences, infectious diseases, and Indigenous Peoples’ sovereignty and livelihoods. More research and awareness of these socioecological interconnections are needed.

The use of fluoride (F^-) for decreasing the prevalence and incidence of tooth decay was the greatest worldwide preventive public health measure of the 20th century. There have been controversial reports about the benefits of the use of F^-, because in small amounts it helps prevent dental caries, but in high concentrations it can be potentially toxic and harmful to dental and systemic health. Since the mid-20th century, F^- has been studied by toxicologists, looking at its deleterious effects in human populations. During the last decade, the interest on the undesirable effects has resurfaced because of the knowledge that it interacts with the cellular system, even in low doses, with a very small safety margin. Acute ingestion of toxic amounts of fluoride can cause corrosive gastric mucosa injury. Also respiratory effects such as bleeding, pulmonary edema, tracheostomy and shortness of breath, have been observed in individuals who inhale hydrogen fluoride. Some researchers had shown that F^- is an oxidizing agent and a well-known reversible enzymatic inhibitor that interferes with the enzyme activity of at least 80 proteins, can altered the intracellular redox equilibrium, lipid peroxidation, as well as, alteration in the gene expression and apoptosis induction. The primary purpose of this review is to examine findings of the action of inorganic F^-, and an overview of hard and soft tissue disturbances, known as fluorosis. The goal of this review is to enhance understanding of the mechanisms by which F^- affects cells with an emphasis on human tissue-specific events.

Red djulis (Chenopodium formosanum) is a native cereal plant in Taiwan; it contains abundant polyphenols, betalian and dietary fiber. The appearance of red djulis is bright red. Therefore, it is also called the “ruby of cereals”. The antioxidative activity of red djulis extract is well-understood. However, the antiaging function still remains unclear. This study examined the potential of red djulis extract for enhancing collagen secretion and preventing cutaneous aging using red djulis extracts. The red djulis extracts are comprised of an abundant active component that can effectively enhance the ability of collagen secretion of dermal fibroblasts, prevent the glycation of collagen and resist the damage of ultraviolet light exposure. After fibroblast treatment with red djulis extracts, TGM1, KRT1, KRT10 and SOD2 genes were up-regulated significantly by 2.3, 4.3, 4.4 and 27.3 times, respectively, compared to those of the control group. Additionally, it can increase COL1A2 gene expression by 43% and decrease MMP9 gene expression 33%. Therefore, it was demonstrated that red djulis extracts affect gene expressions related to the skin barrier, antioxidation and collagen. Moreover, we found positive effects on skin barrier integrity, endogenous antioxidant activity and skin collagen-preservation. The preparation of the red djulis extracts is environmental friendly and can promote the economic value of Chenopodium formosanum; thus, the proposed extract is suitable for applications in the development of food products, especially beverages, skin care and cosmetic products.

The recent SARS-CoV-2 pandemic, which is causing COVID 19 disease, has taught us unexpected lessons about the dangers of human extinction through highly contagious and lethal diseases. As the COVID 19 pandemic is now being controlled by various isolation measures, therapeutics and vaccines, it became clear that our current lifestyle and societal functions may not be sustainable in the long term. We now have to start thinking and planning on how to face the next dangerous pandemic, not just overcoming the one that is upon us now. Is there any evidence that even worse pandemics could strike us in the near future and threaten the existence of the human race? The answer is unequivocally yes. It is not necessary to get infected by viruses of bats, pangolins and other exotic animals that live in remote forests in order to be in danger. Creditable scientific evidence indicates that the human gut microbiota harbor billions of viruses which are capable of affecting the function of vital human organs such as the immune system, lung, brain, liver, kidney, heart etc. It is possible that the development of pathogenic variants in the gut can lead to contagious viruses which can cause pandemics, leading to destruction of vital organs, causing death or various debilitating diseases such as blindness, respiratory, liver, heart and kidney failures. These diseases could result n the complete shutdown of our civilization and probably the extinction of human race. In this essay, I will first provide a few independent pieces of scientific facts and then combine this information to come up with some (but certainly not all) hypothetical scenarios that could cause human race misery, even extinction. I hope that these scary scenarios will trigger preventative measures that could reverse or delay the projected adverse outcomes.

The establishment of a well characterized non-human primate model of Zika virus (ZIKV) infection is critical for the development of medical interventions. In this study, challenging Indian rhesus macaques (IRMs) with ZIKV strains of the Asian lineage resulted in dose dependent peak viral loads between days 2 and 5 post infection; and a robust immune response which protected the animals from homologous and heterologous re-challenge. In contrast, viremia in IRMs challenged with an African lineage strain was below the assays lower limit of quantitation and the immune response was insufficient to protect from re-challenge. These results corroborate previous observations but are contrary to reports using other African strains obviating the need for additional studies to elucidate the variables contributing to the disparities. Nonetheless, the utility of an Asian lineage ZIKV IRM model for countermeasures development was verified by vaccinating animals with a formalin inactivated reference vaccine and demonstrating sterilizing immunity against a subsequent subcutaneous challenge.

Of the roughly 20,000 canonical human protein sequences, as of September 15, 2020, 6,937 proteins have had their full or partial, medium- to high-resolution structures determined by x-ray crystallography or other methods. Which of these proteins dominate the Protein Data Bank (the PDB) and why? In this paper, we list the 273 top human protein structures based on the number of their PDB entries. This set of proteins accounts for more than 40% of all available human PDB entries and represent past trends as well as current status for protein structural biology. We briefly discuss the relationship which some of the prominent protein structures have with protein research as a whole and mention their relevance to human diseases. The top-10 soluble and membrane proteins are all well-known (most of their first structures being deposited more than 30 years ago). Overall, there is no dramatic change in recent trends in the PDB. Remarkably, the number of structure depositions has grown nearly exponentially over the last 10 or more years (with a doubling time of 7 yrs for proteins from all organisms). Growth in human protein structures is slightly faster (at 5.9 yrs, while E.Coli and Mouse+Rat protein structures accumulate more slowly, Zebrafish protein structures are growing most, at a doubling every 3.7 years, albeit starting from only approx. 100 structure entries in 2010). The information may be informative to senior scientists but also inspire researchers who are new to protein science, providing the year 2020 snap-shot for the state of protein structural biology.

The cooperation between humans and robots is becoming increasingly important in our society. Consequently, there is a growing interest in the development of models that can enhance the interaction between humans and robots. A key challenge in the Human-Robot Interaction (HRI) field is to provide robots with cognitive and affective capabilities, developing architectures that let them establish empathetic relationships with users. Several models have been proposed in recent years to solve this open-challenge. This work provides a survey of the most relevant attempts/works. In details, it offers an overview of the architectures present in literature focusing on three specific aspects of HRI: the development of adaptive behavioural models, the design of cognitive architectures, and the ability to establish empathy with the user. The research was conducted within two databases: Scopus and Web of Science. Accurate exclusion criteria were applied to screen the 1007 articles found (at the end 30 articles were selected). For each work, an evaluation of the model is made. Pros and cons of each work are detailed by analysing the aspects that can be improved so that an enjoyable interaction between robots and users can be established.

The microbiota of human breast milk (HBM) contributes to infant gut colonization; however, whether bacterial extracellular vesicles (EVs) are present in HBM or might contribute to this process remains unknown. In the present study, we characterized the HBM microbiota of healthy Korean mothers and measured the key bacteria likely affecting infant gut colonization by analyzing both the microbiota and bacterial EVs. A total of 22 HBM samples were collected from lactating mothers. The DNA of bacteria and bacteria-derived EVs was extracted from each sample. Gene analysis was performed using Illumina MiSeq. Firmicutes accounted for the largest portion among the phyla, followed by Proteobacteria, Bacteroides, and Actinobacteria in both bacteria and bacterial EV samples. At the genus level, Streptococcus (25.1%) and Staphylococcus (10.7%) were predominant in bacterial samples, whereas Bacteroides (9.1%), Acinetobacter (6.9%), and Lactobacillaceae(f) (5.5%) were prevalent in bacterial EV samples. Several genera including Bifidobacterium were significantly positively correlated between the two samples. Our findings reveal the diverse bacterial communities in HBM of healthy lactating mothers and suggest the presence of key bacteria with metabolic activity in HBM and that EVs derived from these bacteria may contribute to the vertical transfer of gut microbiota from mother to infant.

This article presents the study of religious pluralism and decolonization of Indian mind in Russia. The paper analyzes the investigation results concerning psychological features of modern Indian students from universities in Russia and India. For measuring of connection between religious pluralism and decolonization of Indian mind we made socio-psychological investigation of Indian students. We made 254 semi-structured interview with Indian students who are studying in Russia and India. According to the result of investigation that decolonization of Indian mind is connected with the level of religious pluralism. Among the values principles of religious pluralism get more significance and importance in decolonizing mind of Indian students.

Breast cancer treatment mostly revolved around radiation therapy and surgical interventions, these treatments doesn’t provide satisfactory relief to the patients and carry unmanageable side-effects. Nanomaterials show promising results in treating cancer cells and have many advantages such as high biocompatibility, bioavailability and effective therapeutic capabilities. Interestingly, fluorescent magnetic nanoparticles have been used in many biological and diagnostic applications, but there is no report of use of fluorescent magnetic submicronic polymer nanoparticles (FMSP-nanoparticles) in the treatment of human breast cancer cells. In the present study, we have tested the effect FMSP-nanoparticles on human breast cancer cells (MCF-7). We have tested different concentrations (1.25µg/1mL, 12.5µg/mL and 50µg/1mL) of FMSP-nanoparticles in MCF-7 cells and evaluated the nanoparticles response morphometrically. Our results revealed that FMSP-nanoparticles produced a concentration dependent effect on the cancer cells, dose of 1.25µg/mL produced no significant effect on the cancer cell morphology and cell death, whereas dosages of 12.5µg/mL and 50µg/mL respectively showed significant nuclear augmentation, disintegration, chromatic condensation followed by dose dependent cell death. Our results demonstrate FMSP-nanoparticles have ability to induce cell death in MCF-7 cells and may be considered as a potential anti-cancer agent for breast cancer treatments.

We describe a dataset that contains job description published on a popular online website in the information and technology sector. As the website focus mainly on United Kingdom based jobs, the data have a specific focus on this country. It contains 11.501 job vacancies and 13 related meta data information. The dataset is suitable for HR analysis using machine learning techniques such as natural language processing and neural networks.

Free-range sloths living in an urban environment is rare. In this study, human opinions, attitudes and interactions with a population of Bradypus variegatus in a public square were investigated. A questionnaire was applied to people in the square where the sloths reside, and informal, opportunistic observations of human-sloth interactions were made. 95% of respondents knew of the sloths’ existence in the square and 87.8% likes their presence. Opinions about population size differed greatly and younger people were concerned if the square was appropriate place for them. Some human-sloth interactions showed the consequence of lack of biological knowledge. People initiated all sloth-human interactions. The fact that sloths are strictly folivorous has limited their interactions with humans and consequently minimised negative impact of the human-animal interaction on their wellbeing. These results demonstrate that while there is a harmonious relationship between people and sloths, actions in environmental education of the square’s public could be beneficial for the sloths.

Background Reliable and field-applicable diagnosis of schistosome infections in non-human animals is important for surveillance, control, and verification of interruption of human schistosomiasis transmission. This study aimed to summarize uses of available diagnostic techniques through a systematic review and meta-analysis. Methods and principal findings We systematically searched the literature and reports comparing two or more diagnostic tests in non-human animals for schistosome infection. Out of 4,909 articles and reports screened, 18 met our inclusion criteria, four of which were considered in the meta-analysis. A total of 14 techniques (parasitologic, immunologic, and molecular) and nine types of non-human animals were involved in the studies. Notably, four studies compared parasitologic tests (miracidium hatching test (MHT), Kato-Katz (KK), the Danish Bilharziasis Laboratory technique (DBL), and formalin-ethyl acetate sedimentation-digestion (FED-SD)) with quantitative polymerase chain reaction (qPCR), and sensitivity estimates (using qPCR as the reference) were extracted and included in the meta-analyses, showing significant heterogeneity across studies and animals hosts. The pooled estimate of sensitivity was 0.21 (95% confidence interval (CI): 0.03 – 0.48) with FED-SD showing highest sensitivity (0.89, 95% CI: 0.65 – 1.00). Conclusions and significance Our findings suggest that the parasitologic technique FEA-SD and the molecular technique, qPCR, are the most promising field-applicable techniques for schistosome diagnosis in non-human animal hosts. Future studies are needed for validation and standardization of the techniques for real-world field applications.

The paper presents the possibility of using KINECT v2 module to control an industrial robot by means of gestures and voice commands. It describes elements of creating software for off-line and on-line robot control. The application for KINECT module was developed in C# language in Visual Studio environment, while the industrial robot control program was developed in RAPID language in RobotStudio environment. The development of a two-threaded application in RAPID language allowed to separate two independent tasks for the IRB120 robot. The main task of the robot is performed in thread no. 1 (responsible for movement). Simultaneously working thread no. 2 ensures continuous communication with the KINECT system and provides information about the gesture and voice commands in real time without any interference in thread no. 1. The applied solution allows the robot to work in industrial conditions without negative impact of communication task on the time of robot’s work cycles. Thanks to the development of a digital twin of the real robot station, tests of proper application functioning in off-line mode (without using a real robot) were conducted. Obtained results were verified online (on the real test station). Tests of correctness of gesture recognition were carried out, the robot recognized all programmed gestures. Another test carried out was the recognition and execution of voice commands. A difference in the time of task completion between the actual and virtual station was noticed - the average difference was 0.67 s. The last test carried out was to examine the impact of interference on the recognition of voice commands. With a 10dB difference between the command and noise, the recognition of voice commands was equal to 91.43%. The developed computer programs have a modular structure, which enables easy adaptation to process requirements.

Scientific theories on the functioning and dysfunction of the human brain require a good understanding of both its development — before and after birth, and through maturation to adulthood — and its evolution from the ancestral primate brain. Adopting a complex-systems approach, here we propose that the apparent uniqueness of humans’ cognitive capacities might best be understood as emerging from multiple nested “virtuous cycles.” In particular, we propose that the intimate link that exists between oxygen metabolic loops, cortical expansion, and ultimately cognitive and social demands is a key driver of genetic developmental programs for the human brain. Overall, our proposed evolutionary model makes explicit mechanistic links between metabolism, molecular and cellular brain heterogeneity, and behaviour that may in time provide a clearer understanding of brain developmental trajectories and their disorders.

Over the last decade, a significant literature has emerged that advocates the potential of different Additive manufacturing (AM) technologies and printable polymeric materials. Nevertheless, large scale printing and complex geometric shapes, with curvatures and non-planar layer deposition, are a challenging process for the traditional gantry-based machine. The 3 degrees of freedom cartesian configuration restricted their capability to planar layered printing and restricted part dimensions. To date, many researchers have used industrial robots to overcomes this limitation. This review gives the reader a good overview of the FDM technique due to its scalability, cost efficiency and a wide range of material printability. A strong emphasis is laid on the PLA and PLA-based composites as promising materials for the FDM process applications. The second part of this paper links the successful use of these materials in the traditional printing process to large scale printing using the robot-based FDM process. This survey presents representative setups for robot-based AM and works that have been used these setups for non-planar material deposition. Finally, we conclude this paper by identifying opportunities for realizing new functional capabilities by exploiting robot-based AM, and we also present the future trends in this area.

Tuberculosis cause of death worldwide and the leading cause from a single infectious agent, ranking above Human immunodeficiency virus (HIV) and Acquired Immune Deficiency Syndrome (AIDS). The aim of this study is to ascertain the trend of tuberculosis prevalence and the effect of HIV prevalence onl Tuberculosis case in some West African countries from 2000 to 2016 using count panel data regression models. The data used annual HIV and Tuberculosis cases spanning from 2000 to 2016 extracted from online publication of World health Organization (WHO). Panel Poisson regression model and Negative binomial regression model for fixed and random effects were used to analyzed the count data, the result revealed a positive trend in TB cases while increased in HIV cases leads to increase in TB cases in West African countries. Among the competing models used in this study, Panel Negative Binomial Regression Model with fixed effect emerged the best model with log likelihood value of -1336.554. This study recommended that Government and NGOs need more strategies to fight against HIV menace in West Africa as this will in turn reduced TB cases in West Africa.

A COVID-19 disease threatens the population and the economies of the countries significantly. Till now, this pandemic has affected 215 counties or territories. Unavailability of vaccine is the primary concern for the society. To avoid the spread of this disease, social isolation must be preserved and the inter and intra-population movement must be minimized. To reduce the possibility of transmission, the categorization of regions based on susceptibility to COVID-19 infection is a must.Due to the unavailability of a large amount of paper collection for this novel COVID-19 diseases, we used current literature available on a COVID-19 susceptibility of the diabetic patient, human reproductivity, hemodialysis patient’s, pregnant women and meteorological factors and geographical location. Countries in the cold region are more susceptible to the risk of COVID-19 transmission. There was no evidence of the spread of this disease from non-respiratory bodies. Diabetic patients and pregnant women were found to be more susceptible to COVID-19 infection. Anosmia was observed in the majority of the COVID-19 infected cases in European countries. No evidence indicates COVID-19's impact on the human reproductive system explicitly. No cases of vertical transmission of this disease have been observed until now. All the studies available till now is the small scale study. Correlation with something always does not mean causation. There are certain factor like pollution level, temperature Diurnal temperature range, geographical factor, humidity, pollution level, wind speed, population density, medical healthcare facilities social and political factor plays a critical role in transmitting the SARS-COV-2 virus. Besides the adverse effects, it has taught us to shed our selfish goals and to promote the welfare of all.

There is little understanding of patients' experiences and perceptions with satisfaction by health professionals such as medical and dental clinicians. Furthermore, patient satisfaction is not well understood. The objective of this article is to better understand patients' satisfaction with their medical and dental care. The methods of the current article are based on a narrative review of the literature strategy. Patient satisfaction's multidimensional nature has been established since the perceived reasons for satisfaction varied widely among patients. Many aspects of the treatment influence participant satisfaction at different stages of the intervention's process. An improved understanding of the basis for managing patients' expectations with information reiteratively and efficiently may ultimately reduce patients' potential for negative feelings toward the medical and dental treatment experience. The consumerist method may misrepresent the concept of satisfaction in health service.

Generative models for images, audio, text and other low-dimension data have achieved great success in recent years. Generating artificial human movements can also be useful for many applications, including improvement of data augmentation methods for human gesture recognition. The object of this research is to develop a generative model for skeletal human movement, allowing to control the action type of generated motion while keeping the authenticity of the result and the natural style variability of gesture execution. We propose to use a conditional Deep Convolutional Generative Adversarial Network (DC-GAN) applied to pseudo-images representing skeletal pose sequences using Tree Structure Skeleton Image format. We evaluate our approach on the 3D-skeleton data provided in the large NTU RGB+D public dataset. Our generative model can output qualitatively correct skeletal human movements for any of its 60 action classes. We also quantitatively evaluate the performance of our model by computing Frechet Inception Distances, which shows strong correlation to human judgement. Up to our knowledge, our work is the first successful class-conditioned generative model for human skeletal motions based on pseudo-image representation of skeletal pose sequences.

The research work examined the trend of HIV/AIDS, Tuberculosis, and Hepatitis diseases in Plateau state. Annual data from 2003 to 2018 was collected from the department of biostatistics at Plateau State Specialist Hospital (PSSH), Jos. The methods of analysis used are the Poisson Autoregressive Model (PAR(1)) and the Poisson Exponentially Weighted Moving Average Model (PEWMA). The results revealed a significant annual decrease of 23.9% and 4% in Tuberculosis and HIV/AIDS respectively. Furthermore, the results showed a significant annual increase of 46% in Hepatitis. The PEWMA model used revealed that TB increased by 0.02% when there is an increase in HIV but not significant, while Hepatitis significantly aggravates TB by at least 0.24%. Also, there is a significant rise in HIV by 0.85% when TB increases but Hepatitis has no such effect on HIV. Lastly, PEWMA model indicated a rise of 0.5% in Hepatitis cases when there is an increase in TB, but a surge in HIV has no such effect on Hepatitis cases in Jos. The study recommended that fight against TB should be intensified since TB cases significantly affect both HIV and Hepatitis in Jos, Nigeria.

In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analysed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioural and neurological key features and may result restricted to specific tasks of human-robot interaction. Limitations and challenges are discussed to provide meaningful directions for future investigations.

We report on an exploratory study conducted at a graduate school in Sweden with a humanoid robot, Baxter. First, we describe a list of potentially useful capabilities for a robot teaching assistant derived from brainstorming and interviews with faculty members, teachers, and students. These selected capabilities consist of reading, greeting, alerting, remote operation, clarification, and motion. Second, we present feedback on how the robot's capabilities, demonstrated in part with the Wizard of Oz approach, were perceived, and iteratively adapted over the course of several lectures, using the \EP tool. Third, we discuss observations and findings regarding the capabilities and the development process. Our findings suggest that using a social robot as a teaching assistant is promising using the chosen capabilities. We find that personalizing the capabilities and the role of embodiment are important topics to be considered in future work.

Present paper is dealing with the adaptive static balancing of robot or other mechatronic arms that are moving in vertical plane and whose static loads are variable, by using counterweights and springs. Some simple passive and approximate solutions are proposed and an example is shown. The active and exact solutions by using adaptive real time control in the case of unknown variation of static loads are simulated on VIPRO platform developed at Institute of Solid Mechanics of Romanian Academy.

Animal-assisted interventions (AAI) use human-animal interactions to positive effect in various contexts including cancer care. This systematic literature review is the first part of a two-part paper series focusing on the research methods and quantitative results of AAI studies in oncology. We find methodological consistency in the use of canines as therapy animals, in the types of high-risk patients excluded from studies, and in the infection precautions taken with therapy animals throughout cancer wards. The investigated patient endpoints are not significantly affected by AAI, with the exceptions of improvements in oxygen consumption, quality of life, depression, mood, and satisfaction with therapy. The AAI field in oncology has progressed significantly since its inception and has great potential to positively impact future patient outcomes. To advance the field, AAI research in oncology should consistently improve the methodological design of studies, report data more completely, and focus on the therapy animal’s well-being.

Transposable elements (TEs), also known as mobile elements (MEs), are interspersed repeats that constitute a major fraction of the genomes of higher organisms. As one of their important functional impacts on gene function and genome evolution, TEs participate in regulating the expression of genes nearby and even far away at transcriptional and post-transcriptional levels. There are two known principal ways by which TEs regulate expression of genes. First, TEs provide cis-regulatory sequences in the genome with their intrinsic regulatory properties for their own expression making them potential factors for regulating the expression of the host genes. TE-derived cis-regulatory sites are found in promoter and enhancer elements, providing binding sites for a wide range of trans-acting factors. Second, TEs encode for regulatory RNAs with their sequences showed to be present in a substantial fraction of miRNAs and long non-coding RNAs (lncRNAs), indicating the TE origin of these RNAs. Furthermore, TEs sequences were found to be critical for regulatory functions of these RNAs including binding to the target mRNA. TEs thus provide crucial regulatory roles by being part of cis-regulatory and regulatory RNA sequences. Moreover, both TE-derived cis-regulatory sequences and TE-derived regulatory RNAs, have been implicated to provide evolutionary novelty to gene regulation. These TE-derived regulatory mechanisms also tend to function in tissue-specific fashion. In this review, we aim to comprehensively cover the studies regarding these two aspects of TE-mediated gene regulation, mainly focusing on the mechanisms, contribution of different types of TEs, differential roles among tissue types, and lineage specificity, based on data mostly in humans.

The urgency to develop vaccines against Covid-19 is putting pressure on the long and expensive development timelines which are normally required for development of lifesaving vaccines. There is a unique opportunity to take advantage of new technologies, smart and flexible design of clinical trials, and evolving regulatory science to speed up vaccine development against Covid-19 and transform vaccine development altogether.

In this study, we asked participants to answer an online questionnaire about videos showing animal epimeletic behaviours: an individual (a sparrow, an elephant and a macaque) displayed behaviours towards an inanimate conspecific who suddenly got back to conscious at the end of the footage. A fourth video showed a dog-robot kicked by an engineer to demonstrate its stability. After each video, questions were asked to score the degree of anthropomorphism of participants, from mentophobia (no attribution whatever the species) to full anthropomorphism and to measure how close participants are to biological reality (actual scientific knowledge). A first important result is that there is a negative correlation (about 61%) between the anthropomorphism score (AS) and the biological reality one (BRS) showing a wrong statement. The heterogeneity of responses proved that all levels of anthropomorphism are covered from mentaphobia to full anthropomorphism. However, the scores participants attributed to animals differ according to the species shown in the video and to human characteristics. Understanding how one can play with these factors can conduct to better relationships with animals as encourage human-robot interactions. Finally, such reflective anthropomorphism can lead to an increase of human empathy and sociality, finally increasing our humanity.

Designing motion representations for the problem of 3D human action recognition from skeleton sequences is an important yet challenging task. An effective representation should be robust to noise, invariant to viewpoint changes and result in a good performance with low-computational demand. Two main challenges in this task include how to efficiently represent spatio-temporal patterns of skeletal movements and how to learn their discriminative features for classification task. This paper presents a novel skeleton-based representation and a deep learning framework for 3D action recognition using RGB-D sensors. We propose to build an action map called SPMF (Skeleton Posture-Motion Feature), which is a compact image representation built from skeleton poses and their motions. An Adaptive Histogram Equalization (AHE) algorithm is then applied on the SPMF to enhance their local patterns and form an enhanced action map, namely Enhanced-SPMF. For learning and classification tasks, we exploit Deep Convolutional Neural Networks based on the DenseNet architecture to learn directly an end-to-end mapping between input skeleton sequences and their action labels via the Enhanced-SPMFs. The proposed method is evaluated on four challenging benchmark datasets, including both individual actions, interactions, multiview and large-scale datasets. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches on all benchmark tasks, whilst requiring low computational time for training and inference.

Modelling errors, robust stabilization/tracking problems under parameter and model uncertainties complicate the control of the flexible underactuated systems. Chattering-free sliding-mode based input-output control law realizes robustness against the structured and unstructured uncertainties in the system dynamics and avoids excitation of unmodeled dynamics. The main purpose is to propose a robust adaptive solution for stabilizing and tracking direct-drive (DD) flexible robot arms under parameter and model uncertainties, as well as external disturbances. A lightweight robot arm subject to external and internal dynamic effects was taken into consideration. The challenges are compensating actuator dynamics with the inverter switching effects and torque ripples, stabilizing the zero dynamics under parameter/model uncertainties and disturbances while precisely track the predefined reference position. The precise control of this kind of system demands an accurate system model and knowledge of all sources that excite unmodeled dynamics. For this purpose, equations of motion for a flexible robot arm were derived and formulated for the large motion via Lagrange’s method. The goals were determined to achieve high-speed, precise position control, and satisfied accuracy by compensating the unwanted torque ripple and friction that degrades performance through an adaptive robust control approach. The actuator dynamics and their effect on the torque output were investigated due to the transmitted torque to the load side. The high-performance goals, precision&robustness issues, and stability concerns were satisfied by using robust-adaptive input-output linearization-based control law combining chattering-free sliding mode control (SMC) while avoiding the excitation of unmodeled dynamics.

Holographic subsurface radar (HSR) is currently not in widespread usage. This is due to an historical perspective in the ground penetrating radar (GPR) community that the high attenuation of electromagnetic waves in most media of interest, and the inability to apply time-varying gain to the continuous wave (CW) HSR signal precludes sufficient effective penetration depth. While it is true that the fundamental physics of HSR, with its use of a CW signal, does not allow amplification of later (i.e. deeper) arrivals in lossy media (as is possible with impulse subsurface radar — ISR), HSR has distinct some distinctive advantages. The most important of these is the ability to do shallow subsurface imaging with a resolution that is not possible with ISR. In addition, the design of an HSR system is simpler than for ISR due to the relatively low-tech transmitting and receiving antennae. This paper provides a review of the main principles of HSR through an optical analogy and describes possible algorithms for radar hologram reconstruction. We also present a review of the history of development of systems and applications for HSR of the “RASCAN” type which is possibly the only holographic subsurface radar that is produced in lots. Among the subsurface imaging and remote sensing applications considered are humanitarian demining, construction inspection, surveys of historic architecture and artworks, nondestructive testing of dielectric aerospace materials, security applications, paleontology, detection of wood-boring insect damage, and others. Each application is illustrated with relevant data acquired in laboratory and/or field experiments.

In December 2019, pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection emerged in Wuhan City, Hubei Province, China. Early in 2020, the World Health Organization (WHO) announced a new name for the 2019-nCoV-caused epidemic disease: coronavirus disease 2019 (COVID-19) and declared COVID-19 to be the sixth international public health emergency. Cellular co-infection is a critical determinant of both viral fitness and infection outcome and plays a crucial role in shaping the host immune response to infections. In this study, sixty-eight public next-generation sequencing libraries from SARS-CoV-2 infected patients were retrieved from the NCBI Sequence Read Archive database using SRA-Toolkit. Using an alignment-free method based on K-mer mapping and extension, SARS-CoV-2 was identified in all except three patients. Influenza A H7N9 (3/68), Human immunodeficiency virus 1 (1/68), rhabdovirus isolate (3/68), Human metapneumovirus (1/68), coronaviruses NL63 (1/68), Parvovirus (1/68), Simian virus 40 (1/68), and hepatitis virus (1/68) genome sequences were detected in SARS-CoV-2 infected patients.

Kinases play an important role in regulating various intracellular signaling pathways that control cell proliferation, differentiation, survival and other cellular processes and their deregulation causes more than 400 diseases. Consequently, macrocyclization can be considered a noteworthy approach to develop new therapeutic agents for human diseases. Macrocyclization has emerged as an effective drug discovery strategy over the past decade to improve target selectivity and potency of small molecules. Small compounds with linear structures upon macrocyclization can lead to changes in their physicochemical and biological properties by firmly reducing conformational flexibility. A number of distinct protein kinases exhibit similar binding sites. Comparison of protein binding site provides crucial insights for the drug discovery and development. Binding site similarities are helpful to understand polypharmacology, identifying potential off-targets and repurposing of known drugs. In this review, we focused on comparing binding sites of those kinases for which macrocyclic inhibitors are available/studied so far. Furthermore, we calculated the volume of binding site pocket for each targeted kinase and then compared it with the binding site pocket of the kinase for which only acyclic inhibitors were designed till date. Our review and analysis of several explored kinases might be useful to target new protein kinases for macrocyclic drug discovery.

Recruitment is a fundamental aspect of Human Resource Management to drive organizational performance. Traditional recruitment processes, with manual stages, are time-consuming and inefficient. Artificial Intelligence (AI), which demonstrates its potential in various sectors such as healthcare, education and notable cases of ChatGPT, is currently reshaping recruitment by automating tasks to improve efficiency. However, in Thailand, where there is a growing demand for talents, the application of AI in recruitment remains relatively limited. This research focuses on human resources (HR) and recruitment professionals in Thailand, aiming to understand their perspectives on the integration of AI in recruitment. It extended the Unified Theory for Acceptance and Use of Technology (UTAUT) model, customized to suit the specific requirements of Thai recruitment practices. The study explores the factors influencing users' intention to adopt AI in recruitment. Survey questionnaire items were created based on prior literatures and refined with insights from HR and recruitment experts to ensure applicability in the context of recruitment in Thailand. A survey involving 364 HR and recruiting professionals in Bangkok metropolitan area supplied comprehensive responses. The research reveals that several factors, including Perceived Value, Perceived Autonomy, Effort Expectancy, and Facilitating Conditions, significantly impact the intention to adopt AI for recruitment. While Social Influence and Trust in AI Technology do not have a direct influence on intention, Social Influence directly affects Perceived Value. Trust in AI Technology positively influences Effort Expectancy. This study provides valuable benefits for HR and recruitment professionals, organizations and AI developers by offering insights into AI adoption and sustainability, enhancing recruitment processes and promoting the effective use of AI tools in this sector.

Aminoacyl-tRNA synthetases (ARSs) are essential enzymes for translating amino acids for protein synthesis. Their function in pathogen-derived infectious diseases has been well established, which has led to development of small molecule therapeutics. The applicability of ARS inhibitors for other human diseases such as fibrosis has recently been explored in the clinical setting. There are active studies to find small molecule therapeutics for cancers. Studies on central nervous system (CNS) disorders are burgeoning as well. In this regard, we present a concise analysis of the recent development of ARS inhibitors based on small molecules from the discovery research stage to clinical studies as well as a recent patent analysis from the medicinal chemistry point of view.

Technological innovation has led to the development of increasingly efficient and complex industrial plants. To manage this complexity, it is necessary to define an integrated vision of the socio-technological system that includes: technological, human and organizational component. Petrochemicals can be considered one of the most complex socio-technical systems that deserve special attention to high risk management, especially during the emergency conditions. Traditional safety management models only consider static systems, while new resilience engineering models evaluate the performance variability developed between different actions. One of the recent development methods is the Functional Resonance Analysis Method (FRAM) that identifies the pairs between the functions. FRAM unfortunately is a qualitative model, this research integrates this model with the Performance Shaping Factors (PSFs) and with the Bayesian approach to identify the performance variability of the system. The analysis aims to develop a system that improves safety analysis. The proposed model is applied in a case study of an emergency in a petrochemical company.

Pesticides have a vital contribution to the agricultural industry in Australia. However, pesticide applications may not always selective to its target organism. Exposure to pesticide has been associated with negative impacts to human health. One of the ways to reduce pesticide risk to the population and the environment is through government regulations. To put Australia pesticide regulations into perspective, comparisons were made between Australia and other nation’s pesticides regulations. The EU pesticide regulatory system was chosen for comparison with the Australian’s in the aspect of protecting the population from pesticide exposure. The comparison showed that the assessment to authorize a pesticide in Australia is based on the risk while in the EU, the assessment is based on the hazard of the pesticide. A registered active substance in Australia can be authorized for use indefinitely unless it is nominated for reconsideration while the period of registration of an active substance in the EU lasts for 10 years. In addition, no routine systematic chemical residue surveys are conducted in Australia for food commodities. The results highlight areas for improvement in the Australian pesticide regulatory system from the perspective of controlling undue exposure among the general population.