Planning Domain Research Articles

GAN-Based Map Generation Technique of Aerial Image Using Residual Blocks and Canny Edge Detector

As the significance of meticulous and precise map creation grows in modern Geographic Information Systems (GISs), urban planning, disaster response, and other domains, the necessity for sophisticated map generation technology has become increasingly evident. In response to this demand, this paper puts forward a technique based on Generative Adversarial Networks (GANs) for converting aerial imagery into high-quality maps. The proposed method, comprising a generator and a discriminator, introduces novel strategies to overcome existing challenges; namely, the use of a Canny edge detector and Residual Blocks. The proposed loss function enhances the generator’s performance by assigning greater weight to edge regions using the Canny edge map and eliminating superfluous information. This approach enhances the visual quality of the generated maps and ensures the accurate capture of fine details. The experimental results demonstrate that this method generates maps of superior visual quality, achieving outstanding performance compared to existing methodologies. The results show that the proposed technology has significant potential for practical applications in a range of real-world scenarios.

Towards eco-political becoming: Planning rural livelihoods in a more-than-human world

Emerging works within planning invite scholars to engage with a more-than-human approach. However, scholars note that such an approach is yet to gain broader acceptance within planning communities. I argue that the issue of acceptance is rooted in a lack of clear focus on how a more-than-human approach can have concrete effects on accepted domains of planning. Therefore, I introduce the analytic of eco-political becoming, which foregrounds the complex effects of the ecological on the sociopolitical . I present a case of eco-political becoming in planning rural livelihoods, where Indigenous communities engaged in rearing silk, find themselves entangled with crows. As the communities attempt to deter the crows from eating silk moth larvae, the crows in-turn disrupt two key sociopolitical categories and processes in the village—gender and caste. The case highlights sociopolitical outcomes of human-nonhuman entanglement that emerge out of economic planning. I propose that the analytical tool of eco-political becoming enables scholars to focus on the complex effects of nonhuman actors on the socially constructed identities and hierarchies considered important by the larger planning community.

Review of Quality Audit in a Radiation Oncology Department in a Tertiary Care Hospital- 2 Year-Results.

This study aimed to report the findings of comprehensive weekly quality audit of radiotherapy procedures conducted at our institute, assessing various domains including clinical notes, contour delineation, treatment planning, prescription accuracy, patient-specific quality assurance, and treatment set-up checks. The audit protocol, based on the Peer Review Audit Tool (PRAT) from the Royal Australian and New Zealand College of Radiologists, evaluated six critical parameters: clinical decisions, contours, treatment prescriptions, plan evaluation, plan quality assurance, and daily treatment set-ups. Patients were assessed for changes categorized as no change, minor change, or major change. The audit panel comprised radiation oncologists, medical physicists, and therapists. The audit is conducted on a weekly basis, including patients who initiated treatment within the preceding week. Between December 2020 to October 2023, 1831 cases were treated with radiotherapy in our institution and all of them were included in the quality audit. Overall, majority of patients exhibited no changes across various domains, with minor adjustments noted in a small percentage of cases. Notably, the clinical note check domain showed 99% adherence to standards, while contour delineation and treatment planning domains displayed minor adjustments in 2.7% and 1.7% of cases, respectively. Prescription accuracy and patient-specific quality assurance demonstrated high compliance, with minimal changes observed. Treatment set-up checks revealed minor variations in 0.3% and major changes in 0.2% of cases. This study underscores the importance of comprehensive quality audits in radiation oncology to ensure precision and consistency in treatment delivery. By identifying potential areas for improvement and maintaining high standards across all stages of the radiotherapy process, such audits play a crucial role in optimizing treatment outcomes and enhancing patient safety.

GROUNDWATER MANAGEMENT: INTEGRATING GEOLOGICAL AND HYDROLOGICAL DATA FOR EFFECTIVE DECISION MAKING

The most valuable natural resource for all of humanity, not just for a state or nation, is water. A country's ability to prosper largely rests on how wisely it uses this resource. Thus, water, which flows in rivers and streams, can be said to be a nation's primary asset. This proves rivers are important, and further justification is needed to emphasise how important they are. The global acceptance of river basins as planning and management domains stems from the fact that water transcends national boundaries. India's rivers are one of its most defining characteristics; the people of India place great religious significance on them. Since groundwater is the most abundant freshwater resource, it is essential to pinpoint the groundwater potential zones for the ongoing and sustainable growth of socioeconomic and agricultural advances. Hard, fractured rock aquifers underlie most of south India's densely inhabited areas. The meagre amount of freshwater that these aquifers provide is the only source that people may use for cultivation and drinking. Because of the diverse nature of the aquifer owing to varied composition, degree of weathering, and density of fracturing, groundwater conditions in hard rock terrain are multivariate. Therefore, a thorough understanding of the distribution and features of the aquifer under the various hydrogeologic circumstances of the research region is necessary for the long-term utilisation of groundwater resources. This study's primary goal is to evaluate the possibility for groundwater management utilising hydrological and geological investigations.

Evidencing the challenges of care delivery for people with intellectual disability and epilepsy in England by using the Step Together toolkit.

People with intellectual disability (PwID) and epilepsy have increased premature and potentially preventable mortality. This is related to a lack of equitable access to appropriate care. The Step Together guidance and toolkit, developed with patient, clinical, charity and commissioning stakeholders, allows evaluation and benchmarking of essential epilepsy service provision for PwID in eight key domains, at a care system level. To evaluate care provisions for adult PwID and epilepsy at a system level in the 11 integrated care systems (ICSs) of the Midlands, the largest NHS England region (population: approximately 11 million), using the Step Together toolkit. Post training, each ICS undertook its benchmarking with the toolkit and submitted their scores to Epilepsy Action, a national UK epilepsy charity, who oversaw the process. The outcomes were analysed descriptively to provide results, individual and cumulative, at care domain and system levels. The toolkit was completed fully by nine of the 11 ICSs. Across all eight domains, overall score was 44.2% (mean 44.2%, median 43.3%, range 52.4%, interquartile range 23.8-76.2%). The domains of local planning (mean 31.1%, median 27.5%) and care planning (mean 31.4%, median 35.4%) scored the lowest, and sharing information scored the highest (mean 55.2%, median 62.5%). There was significant variability across each domain between the nine ICS. The user/carer participation domain had the widest variation across ICSs (0-100%). The results demonstrate a significant variance in service provision for PwID and epilepsy across the nine ICSs. The toolkit identifies specific areas for improvement within each ICS and region.

Treatment adherence and quality of life in colombian patients with lupus nephritis.

As with many other chronic diseases, systemic lupus erythematosus (SLE) and lupus nephritis (LN) have significant impacts on the health-related quality of life (HRQoL). Medication non-adherence is a significant challenge in the management of SLE, with consistently up to 75% of patients being non-adherent with their SLE medications. There is a need to assess the patient's perspective using patient-reported outcomes (PROs) to better understand the current impact of LN on HRQoL and treatment adherence in our region. The aim of this study was to explore the relationship between HRQoL and treatment adherence in patients with LN from the Colombian Caribbean. A cross-sectional study was conducted from June to December 2022, including patients with biopsy-proven LN. HRQoL and treatment adherence were assessed using the Lupus Quality of Life (LupusQoL) and the Compliance Questionnaire in Rheumatology 19 (CQR19) instruments, respectively. Patients were categorized as adherent or non-adherent based on medication intake (defined as >80% correct dosage). Principal component analysis (PCA) was employed to identify principal components between adherent and non-adherent patients. A total of 42 patients with LN were included. Of these, 38 (90%) were female, and the mean age was 31 ± 10 years. Proliferative class IV was the predominant histopathological profile (90%). Twenty-five (60%) patients were categorized as non-adherent. Across all LupusQoL domains, a comprehensive range of responses was observed. Pain, planning, and intimate relationships domains remained unaffected, while burden to others domain had the lowest score. Poorer planning score correlated with older age (r = -0.72; p < .05) and longer disease duration (r = -0.74; p < .05). SLEDAI-2K correlated with the pain domain (r = -0.78; p < .05). Non-adherent patients exhibited significantly worse pain domain scores compared to adherent counterparts (p < .05). PCA showed strong interactions between planning and pain, as well as between physical health and body image domains. LupusQoL pain domain scores were significantly worse in non-adherent patients compared to adherent patients. Effective pain management could be a determinant in HRQoL and treatment adherence rates in our population.

An adaptive operation planning and EBO-BPNN optimization method for decision support systems

Formulating a course of action (COA) before a combat is crucial for operational command. Research in command and control (C2) artificial intelligence is currently focused on using intelligent auxiliary decision-making methods to implement COA. This paper proposes a COA planning method based on line of operation (LOO) and uses planning domain definition language (PDDL) to describe combat scenarios and COA. Following the effect-based optimization (EBO) principle, an effect evaluation model for COA was constructed, and dynamic bayesian networks (DBNs) was used to determine the reasoning and calculate the results of the effect evaluation network. To further improve the execution efficiency of the effect evaluation model in practical applications, the network was optimized through a back propagation neural network (BPNN). Relevant experiments based on the coordinated distributed air defense and anti-missile scenario were carried out using the LOO model to complete the planning of COA. A BPNN evaluation model based on the DBNs evaluation model was built. After training and fine-tuning, it achieved similar evaluation results, with a mean absolute percentage error (MAPE) of less than 0.02%. Compared with the DBNs model, the BPNN model achieved an efficiency improvement of no less than 65%, effectively reducing the consumption of computing resources. This research is the first time to realize the modeled description of COA planning, automatic evaluation, and calculation optimization of COA effects. It can support the development of decision support systems (DSS) and has the potential for practical application.

Validation of the Basic Psychological Needs Satisfaction scale in transport for urban commuters (BPNS-TUC) in Spain

The aim of this paper is to present a validity and reliability study of the translation into Spanish and adaptation to the transport planning domain of the Basic Psychological Needs Satisfaction scale to assess autonomy, competence, and relatedness satisfaction of urban commuters associated to the use of habitual travel modes, expanding past studies that only focused on a particular population of study and type of travel behavior. An explanatory analysis of the main relations found is also reported, including micro-mobility and shared travel options. To this end, data collected by an online survey during the summer of 2021 from 949 respondents who provided valid data is used. The average age of respondents was 42, and women were slightly underrepresented (40.4%). Factor structure is analyzed and a three-factor ESEM model supports the definition of the constructs. Internal consistency indices show adequate values for the three factors. No statistically significant differences of factor mean values are observed according to sex. The youngest respondents associate the highest values of satisfaction to travel modes when commuting, while the old adults score the lowest values. Active travel modes are associated to the highest values of satisfaction. On the other hand, urban bus is related to the lowest values of satisfaction. Private e-scooter and shared mobility are associated with a higher satisfaction than driving a private car. Future work and practical applications are identified.

Meteorological effects of ventilation corridor in central urban areas: A case study of Wuhan

How ventilation corridors affect urban climate is attracting researchers' attention. Taking the inland Chinese city of Wuhan as an example, this paper first uses remote sensing image technology to evaluate the urban thermal environment. Additionally, based on the GIS/RS spatial analysis method, the ventilation corridors in the central urban area are identified and constructed. Finally, the mesoscale meteorological model WRF-UCM is used to simulate four cases with different corridor forms to explore the impact of different corridors on the climate environment in Wuhan during the summer. The results indicate that: (1) The WRF-UCM model, when coupled with LCZ classification, can significantly improve the accuracy of mesoscale urban canopy meteorological field simulations. (2) The water corridors located in the central urban area can effectively regulate the temperature and wind environment during summer. In the high-temperature period of the day, the average temperature in the central city decreases by 0.3–0.4 °C, the heat island proportion index decreases by 1.61 %, and the strong heat island proportion index decreases by 1.89 % in the afternoon. During the period of low temperature, the average wind speed in the central urban area increased by 0.05 m/s increase, and even increased by 0.1 m/s. (3) The specific humidity value of the green corridor is reduced by 0.0000136 kg/kg in comparison to the construction land in the corridor, while the water corridor can increase by 0.000133 kg/kg. If the two kinds of surface, water and green land, are organically combined in the corridor, it will be able to improve the hot and humid conditions in Wuhan in summer. (4)Low-rise and low-density construction land as the corridors in the central urban area can not improve the urban thermal and wind environment. Through an attempt to conduct a complete workflow of urban heat island analysis, ventilation corridor identification and setting, urban climate simulation, analysis and summary, the authors believe that it is an effective set of working methods in sustainable urban planning, design, and policy-making. The implementation of pertinent research findings in the domain of urban planning and design demonstrates its universal applicability and has the potential to extend to analogous research and practice.

The Impact of COVID-19 on Neuropsychological and Emotional-Behavioural Development in a Group of 8- and 9-Year-Old Children.

Introduction: The rapid spread of the COVID-19 pandemic has had a significant impact on the psychological well-being of millions of people around the world, and even more so among children. Contracting SARS-CoV-2, resulting in home confinement and restrictions on daily and school activities, led to negative effects on the mental health of the paediatric population. Although children suffering from COVID-19 had milder general symptoms compared to adults, impairments in cognitive, neuropsychological, and emotional-behavioural development were noted. Objective: The main aim of the present study was to detect possible changes in the neuropsychological and emotional-behavioural development of children after infection with the SARS-CoV-2 virus. The second aim was to investigate possible relationships between cognitive abilities and psychosocial characteristics. Methods: A total of 40 patients aged 8-9 years were recruited and divided into two groups: children who contracted (CG) and did not contract (NCG) SARS-CoV-2. The BVN 5-11 (Neuropsychological evaluation battery for developmental age from 5 to 11 years) instrument was administered to assess attention, memory, verbal recall, planning, phonemics, and categorical fluency domains in the paediatric population. Data on changes in emotional-behavioural profile and daily activities were collected through a questionnaire to parents. Results: The Wilcoxon signed-rank test revealed a significant change in mood after the COVID-19 period only in the CG participants (p = 0.019). However, the neuropsychological performance of the two identified groups on BVN 5-11 sub-items was below the cutoff of clinical significance. Correlations were found between sub-items of the BVN 5-11 battery, extracurricular activities, and children's psycho-motor development. Significant positive correlations were observed between Naming on visual presentation and Reading time (p = 0.006), backward digit span and time of motor activity (p = 0.009), Visual attention and Reading time (p = 0.048), and Phonemic fluency and time observed using devices (p = 0.030). Positive statistically significant correlations were also found between Mood and Free behaviour (p = 0.000), between Mood and Structured behaviour (p = 0.005), and between Mood and peer Interaction (p = 0.013). Conclusions: SARS-CoV-2 infection negatively affected the emotional development of children contracting the virus. The neuropsychological functioning of the paediatric population was influenced by psychosocial variables and time spent on daily activities, which played a protective role in children's cognitive development.

What Factors Revitalize the Street Vitality of Old Cities? A Case Study in Nanjing, China

Urban street vitality has been a perennial focus within the domain of urban planning. This study examined spatial patterns of street vitality in the old city of Nanjing during working days and weekends using real-time user datasets (RTUDs). A spatial autoregressive model (SAM) and a multiscale geographically weighted regression (MGWR) model were employed to quantitatively assess the impact of various factors on street vitality and their spatial heterogeneity. This study revealed the following: (1) the distribution of street vitality in the old city of Nanjing exhibited a structure centered around Xinjiekou, with greater regularity and predictability in street vitality on working days than on weekends; (2) eight variables, such as traffic location, road density, and functional density, are positively associated with street vitality, whereas the green view index is negatively associated with street vitality, and commercial location benefits street vitality at weekends but detracts from street vitality on working days; and (3) the influence of variables such as traffic location and functional density on street vitality is contingent on their spatial position. Based on these results, this study provides new strategies to enhance the street vitality of old cities.

Towards a Regenerative Future: Understanding Practical Implementation, Research, and Benchmarking in the Built Environment

The 73rd Session of the United Nations in 2019 marked a pivotal moment, emphasising the urgent need for sustainable development to avert irreversible climate change impacts within a mere 11 years. Vulnerable communities have been experiencing food security and ecosystem crises since the 1990s, and the IPCC predicts that environmental damage will lead to 200 million permanent displacements by 2050. Built environment professionals such as those in architecture and construction carry a moral obligation to act as critical agents of systemic change, driving the study’s investigation into “Regenerative Design” with a focus on practical implementation, application, research, and benchmarking. The study conducted a comprehensive search and scoping review of peer-reviewed literature and an industry-specific audience poll at a built-environment professional summit. The findings reveal an inconsistent application and limited comprehension of “regenerative” among industry professionals. The study poll of key stakeholders in the Australian built environment context, accentuates the lack of consensus surrounding regenerative design’s definition. This research contributes to interdisciplinary knowledge, bridging domains of education, design, construction, planning, urban development, and policy. It underscores the need for more extensive research and highlights the absence of consensus in defining regenerative design. By shedding light on these aspects, the study aims to galvanise collective action and foster transformative change towards a sustainable future.

A framework for neurosymbolic robot action planning using large language models.

Symbolic task planning is a widely used approach to enforce robot autonomy due to its ease of understanding and deployment in engineered robot architectures. However, techniques for symbolic task planning are difficult to scale in real-world, highly dynamic, human-robot collaboration scenarios because of the poor performance in planning domains where action effects may not be immediate, or when frequent re-planning is needed due to changed circumstances in the robot workspace. The validity of plans in the long term, plan length, and planning time could hinder the robot's efficiency and negatively affect the overall human-robot interaction's fluency. We present a framework, which we refer to as Teriyaki, specifically aimed at bridging the gap between symbolic task planning and machine learning approaches. The rationale is training Large Language Models (LLMs), namely GPT-3, into a neurosymbolic task planner compatible with the Planning Domain Definition Language (PDDL), and then leveraging its generative capabilities to overcome a number of limitations inherent to symbolic task planners. Potential benefits include (i) a better scalability in so far as the planning domain complexity increases, since LLMs' response time linearly scales with the combined length of the input and the output, instead of super-linearly as in the case of symbolic task planners, and (ii) the ability to synthesize a plan action-by-action instead of end-to-end, and to make each action available for execution as soon as it is generated instead of waiting for the whole plan to be available, which in turn enables concurrent planning and execution. In the past year, significant efforts have been devoted by the research community to evaluate the overall cognitive capabilities of LLMs, with alternate successes. Instead, with Teriyaki we aim to providing an overall planning performance comparable to traditional planners in specific planning domains, while leveraging LLMs capabilities in other metrics, specifically those related to their short- and mid-term generative capabilities, which are used to build a look-ahead predictive planning model. Preliminary results in selected domains show that our method can: (i) solve 95.5% of problems in a test data set of 1,000 samples; (ii) produce plans up to 13.5% shorter than a traditional symbolic planner; (iii) reduce average overall waiting times for a plan availability by up to 61.4%.

Piloting the adaptation of the Kaufman Assessment Battery for Children-2 nd edition (KABC-II) to assess school-age neurodevelopment in rural Zimbabwe.

Neurodevelopment assessment tools for low-resource settings are urgently needed. However, most available tools were developed in high-income settings and may lack cross-cultural validity. We piloted and adapted two subtests within the planning domain of the Kaufman Assessment Battery for Children-2nd edition (KABC-II) for use in rural Zimbabwean children aged 7years. After initial assessments of face validity, we created 4 substitutions for the story completion subtest and 7 additions for the pattern reasoning subtest through a co-design process with fieldworkers and child development experts. To assess how successful the changes were, T-tests adjusting for unequal variances were used to compare scores between the original and adapted versions of the same subtest. ANOVA and pairwise analysis was performed to compare the performance of KABC-II subtests across domains. Intraclass correlation coefficient was calculated to explore the variability between domains. Initial test scores on the planning domain were significantly lower than the other three domains of learning, sequential memory and simultaneous reasoning (P<0.001) in 50 children (mean age 7.6(SD 0.2) years). Modified subtests were administered to another 20 children (mean age 7.6(SD 0.2) years), who showed story completion scores that were 0.7 marks higher (95% CI 0.0, 1.4; P=0.05) and pattern reasoning scores 1.8 marks higher (95% CI 0.5, 3.2; P=0.01). Overall, the planning domain mean score increased from 8.1 (SD 2.9) to 10.6 (SD 3.4). The intra class correlation coefficient between all four KABC-II domains was initially 0.43 (95% CI 0.13, 0.64) and after modification was 0.69 (95% CI 0.37, 0.87), suggesting an increase in the construct validity. The KABC-II planning domain was successfully adapted to improve cross-cultural validity. Construct validity was enhanced, based on increased inter-correlations among scales. The process of co-design to modify tests for new settings may be beneficial for other commonly used neurodevelopmental tools.

The COVID-19 pandemic's impact on user consumption: A shift towards very efficient load forecasting

The COVID-19 pandemic has prompted significant shifts in energy consumption patterns, necessitating a departure from traditional data analysis methods. Accurate energy forecasting models play a crucial role in various domains of planning and energy management. It is possible to predict the demand for electricity consumption using a variety of techniques. Fundamentally, this research comprises two main components. The first phase assesses the influence of the COVID-19 pandemic on user consumption patterns through time series analysis. The subsequent segment is dedicated to long-term load forecasting, employing both statistical methods termed as Holt-Winters and Prophet algorithms and an Artificial Neural Network approach known as Long Short-Term Memory. These approaches aim to predict electricity demand amid the diverse and intricate context of the COVID-19 pandemic. It utilizes both conventional time series forecasting methods and advanced Artificial Intelligence models to enhance the accuracy of load forecasting. Since rapid societal and economic shifts are unpredictable during a pandemic, methods based on Artificial Intelligence are becoming extremely important due to its high precision. Though, it is problematic to conduct load prediction with high accuracy because of influencing factors like climatic, socioeconomic, and seasonal aspects. Finally, using hourly-driven power consumption data from Houston, Texas, USA, the Long Short-Term Memory model outperforms the Holt-Winters and Prophet models in a test of generalizability and accuracy. It is possible to verify the accuracy of experimental findings by analyzing the resulting constraints and influential factors.

Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities.

The global prevalence of obesity presents a pressing challenge to public health and healthcare systems, necessitating accurate prediction and understanding for effective prevention and management strategies. This article addresses the need for improved obesity prediction models by conducting a comprehensive analysis of existing machine learning (ML) and deep learning (DL) approaches. This study introduces a novel hybrid model, Attention-based Bi-LSTM (ABi-LSTM), which integrates attention mechanisms with bidirectional Long Short-Term Memory (Bi-LSTM) networks to enhance interpretability and performance in obesity prediction. Our study fills a crucial gap by bridging healthcare and urban planning domains, offering insights into data-driven approaches to promote healthier living within urban environments. The proposed ABi-LSTM model demonstrates exceptional performance, achieving a remarkable accuracy of 96.5% in predicting obesity levels. Comparative analysis showcases its superiority over conventional approaches, with superior precision, recall, and overall classification balance. This study highlights significant advancements in predictive accuracy and positions the ABi-LSTM model as a pioneering solution for accurate obesity prognosis. The implications extend beyond healthcare, offering a precise tool to address the global obesity epidemic and foster sustainable development in smart cities.

The Divergence between Self- and Preceptor-Assessments of Student Performance during Advanced Pharmacy Practice Experiences.

(1) Objectives: A divergence in self- and preceptor-evaluations of clinical skills has been noted during Advanced Pharmacy Practice Experiences (APPEs). The goal of this study was to determine the domains of overestimation of clinical skills by students during their APPE rotations. (2) Methods: Preceptor-assigned grades for APPE rotations from 2017-2022 were analyzed to identify instances of letter grade B or lower. The self- and preceptor-evaluations of APPE rotation were compared to determine the domains of divergence in evaluation between students and preceptors. (3) Results: Between 2017 and 2022, 305 student APPE rotations were graded as B or lower (~14%) by the preceptors. A statistically significant difference was noted between self- and preceptor-assigned letter grades across all practice settings including ambulatory patient care, community pharmacy, general medicine patient care, hospital/health system pharmacy, and special population patient care APPE rotations. In addition, examining the self- and preceptor evaluation rubric for these rotations revealed a statistically significant overestimation of clinical skills by students in all 9 domains of APPE evaluation. Finally, the divergence in the rating of clinical skills between student- and preceptor evaluation was found to be highest in the domains of planning and follow-up of patient care, disease knowledge, and communication with patients. (4) Conclusions: Students who fail to exhibit exemplary practice readiness during APPEs tend to overestimate their clinical skills in all domains of APPE evaluation. The results from our study support the need for additional avenues to assist in the identification of deficits in student learning before APPEs to increase their self-awareness (metacognition).

Using Earley Parser for Verification of Totally Ordered Hierarchical Plans

Hierarchical planning extends classical planning by capturing the hierarchical structure of tasks via decomposition of tasks to subtasks. Hierarchical plan verification is the problem of determining whether a given plan is valid according to that structure. As decomposition trees in totally ordered hierarchical planning domains resemble parsing trees of context-free grammars, one may exploit the techniques for context-free grammars also for hierarchical plans. In this paper, we study how the Earley parser, proposed for checking whether a given word belongs to a language defined by context-free grammar, can be extended to verification of totally ordered hierarchical plans.

The Degree of Human Relations Practice in Leadership among Kindergarten Supervisors and Its Relationship to Job Performance in light of the Corona Crisis Management of Nannies inside the Green Line

The study aimed to discover thet degree of practicing the human relations of kindergartens’ supervisors and their relationship with job performance during the Corona crisis among nannies within the green line. The Researchers used the descriptive correlational survey methodology. The study sample consisted of (386) educators, they were randomly selected. Two questionnaires were used to collect information. The first was used to measure the degree of human relations use in leadership. It consisted of (24) items divided into (3) domains, which were (human interaction, moral stimulation, and effective communication). The second was used to measure the level of job performance. It consisted of (21) items divided into (3) domains of (planning and executing tasks, decision-making, performance, and continuity assessment). The results showed a strong relationship between kindergarten supervisors and their relationship with job performance. According to these results, the researchers recommended organizing training courses to enhance the human relations leadership of the supervisors.

Enhancing awareness of industrial robots in collaborative manufacturing

The diffusion of Human-Robot Collaborative cells is prevented by several barriers. Classical control approaches seem not yet fully suitable for facing the variability conveyed by the presence of human operators beside robots. The capabilities of representing heterogeneous knowledge representation and performing abstract reasoning are crucial to enhance the flexibility of control solutions. To this aim, the ontology SOHO (Sharework Ontology for Human-Robot Collaboration) has been specifically designed for representing Human-Robot Collaboration scenarios, following a context-based approach. This work brings several contributions. This paper proposes an extension of SOHO to better characterize behavioral constraints of collaborative tasks. Furthermore, this work shows a knowledge extraction procedure designed to automatize the synthesis of Artificial Intelligence plan-based controllers for realizing flexible coordination of human and robot behaviors in collaborative tasks. The generality of the ontological model and the developed representation capabilities as well as the validity of the synthesized planning domains are evaluated on a number of realistic industrial scenarios where collaborative robots are actually deployed.