Dynamic Changes In Environment Research Articles

Dynamic changes of food environment: In and out of COVID-19 pandemic

The outbreak of the COVID-19 pandemic has precipitated food crises worldwide, prompting a re-examination of the resilience of the urban food environment. While previous research on the urban food environment has predominantly focused on Western contexts, scant attention has been given to China. This study takes Shenzhen, China as an example to establish a food environment evaluation framework centered on accessibility, diversity, and healthiness factors, aiming to analyze the dynamic changes of the food environment during normal and pandemic periods. By using the GA optimization algorithm, some convenience stores are transformed into self-pickup points (SPPs), which is expected to eliminate the deserts risk areas (DRAs) with low cost and high efficiency. The findings reveal a distinctive “center-periphery” spatial structure characterizing the food environment in Shenzhen, and the improvement of healthiness plays a crucial role in sustaining food oases and ameliorating food swamps. This research provides methods for improving the resilience of the food environment during the pandemic across diverse nations, bolstering the security of urban lifeline systems.

Physical-layer security for primary users in 5G underlay cognitive radio system via artificial-noise-aided by secondary users

The advent of the Internet of Things (IoT) has revolutionized connectivity by interconnecting a vast array of devices, underscoring the critical need for robust data security, particularly at the Physical Layer Security (PLS). Ensuring data confidentiality and integrity during wireless communications poses a primary challenge in IoT environments. Additionally, within the constrained frequency bands available, Cognitive Radio Networks (CRNs) has emerged as an urgent necessity to optimize spectrum utilization. This technology enables intelligent management of radio frequencies, enhancing network efficiency and adaptability to dynamic environmental changes. In this research, we focus on examining the PLS for the primary channel within the underlying CRNs. Our proposed model involves a primary source-destination pair and a secondary transmitter-receiver pair sharing the same frequency band simultaneously. In the presence of a common eavesdropper, the primary concern lies in securing the primary link communication. The secondary user (SU) acts as cooperative jamming, strategically allocating a portion of its transmission power to transmit artificial interference, thus confusing the eavesdropper and protecting the primary user's (PU) communication. The transmit power of the SU is regulated by the maximum interference power tolerated by the primary network's receiver. To evaluate the effectiveness of our proposed protocol, we develop closed-form mathematical expressions for intercept probability ( P int ) and outage probability (OP) along the primary communication link. Additionally, we derive mathematical expressions for OP along the secondary communications network. Furthermore, we investigate the impact of transmit power allocation on intercept and outage probabilities across various links. Through both simulation and theoretical analysis, our protocol aims to enhance protection and outage efficiency for the primary link while ensuring appropriate secondary OP, thereby contributing to advancements in IoT security and spectrum management.

Quantum Inspired Chaotic Salp Swarm Optimization for Dynamic Optimization

Many real-world problems are dynamic optimization problems that are unknown beforehand. In practice, unpredictable events such as the arrival of new jobs, due date changes, and reservation cancellations, changes in parameters or constraints make the search environment dynamic. Many algorithms are designed to deal with stationary optimization problems, but these algorithms do not face dynamic optimization problems or manage them correctly. Although some of the optimization algorithms are proposed to deal with the changes in dynamic environments differently, there are still areas of improvement in existing algorithms due to limitations or drawbacks, especially in terms of locating and following the previously identified optima. With this in mind, we studied a variant of SSA known as QSSO, which is integrating the principles of quantum computing. An attempt is made to improve the overall performance of standard SSA to deal with the dynamic environment effectively by locating and tracking the global optima for DOPs. This work is an extension of the proposed new algorithm QSSO, known as the Quantum-inspired Chaotic Salp Swarm Optimization (QCSSO) Algorithm, which is detailing the various approaches taken into consideration while solving DOPs. A chaotic operator is employed with quantum computing to respond to change and guarantee to increase individual searchability by improving population diversity and the speed at which the algorithm converges. We experimented by evaluating QCSSO on a well-known generalized dynamic benchmark problem (GDBG) provided for CEC 2009, followed by a comparative numerical study with well-regarded algorithms. As promised, the introduced QCSSO is discovered, and a rival algorithm for DOPs.

Proteomic analysis reveals dynamic changes in cloacal fluid composition during the reproductive season in a sexually promiscuous passerine

Cryptic female choice (CFC) is a component of postcopulatory sexual selection that allows females to influence the fertilization success of sperm from different males. While its precise mechanisms remain unclear, they may involve the influence of the protein composition of the female reproductive fluids on sperm functionality. This study maps the protein composition of the cloacal fluid across different phases of female reproductive cycle in a sexually promiscuous passerine, the barn swallow. Similar to mammals, the protein composition in the female reproductive tract differed between receptive (when females copulate) and nonreceptive phases. With the change in the protein background, the enriched gene ontology terms also shifted. Within the receptive phase, distinctions were observed between proteomes sampled just before and during egg laying. However, three proteins exhibited increased abundance during the entire receptive phase compared to nonreceptive phases. These proteins are candidates in cryptic female choice, as all of them can influence the functionality of sperm or sperm-egg interaction. Our study demonstrates dynamic changes in the cloacal environment throughout the avian breeding cycle, emphasizing the importance of considering these fluctuations in studies of cryptic female choice.

Spatiotemporal dynamics of land use/land cover (LULC) changes and its impact on land surface temperature: A case study in New Town Kolkata, eastern India

Rapid urbanization creates complexity, results in dynamic changes in land and environment, and influences the land surface temperature (LST) in fast-developing cities. In this study, we examined the impact of land use/land cover (LULC) changes on LST and determined the intensity of urban heat island (UHI) in New Town Kolkata (a smart city), eastern India, from 1991 to 2021 at 10-a intervals using various series of Landsat multi-spectral and thermal bands. This study used the maximum likelihood algorithm for image classification and other methods like the correlation analysis and hotspot analysis (Getis–Ord Gi* method) to examine the impact of LULC changes on urban thermal environment. This study noticed that the area percentage of built-up land increased rapidly from 21.91% to 45.63% during 1991–2021, with a maximum positive change in built-up land and a maximum negative change in sparse vegetation. The mean temperature significantly increased during the study period (1991–2021), from 16.31°C to 22.48°C in winter, 29.18°C to 34.61°C in summer, and 19.18°C to 27.11°C in autumn. The result showed that impervious surfaces contribute to higher LST, whereas vegetation helps decrease it. Poor ecological status has been found in built-up land, and excellent ecological status has been found in vegetation and water body. The hot spot and cold spot areas shifted their locations every decade due to random LULC changes. Even after New Town Kolkata became a smart city, high LST has been observed. Overall, this study indicated that urbanization and changes in LULC patterns can influence the urban thermal environment, and appropriate planning is needed to reduce LST. This study can help policy-makers create sustainable smart cities.

PD-L2 drives resistance to EGFR-TKIs: dynamic changes of the tumor immune environment and targeted therapy.

There is a lack of effective treatments to overcome resistance to EGFR-TKIs in EGFR mutant tumors. A deeper understanding of resistance mechanisms can provide insights into reducing or eliminating resistance, and can potentially deliver targeted treatment measures to overcome resistance. Here, we identified that the dynamic changes of the tumor immune environment were important extrinsic factors driving tumor resistance to EGFR-TKIs in EGFR mutant cell lines and syngeneic tumor-bearing mice. Our results demonstrate that the acquired resistance to EGFR-TKIs is accompanied by aberrant expression of PD-L2, leading a dynamic shift from an initially favorable tumor immune environment to an immunosuppressive phenotype. PD-L2 expression significantly affected EGFR mutant cell apoptosis that depended on the proportion and function of CD8+ T cells in the tumor immune environment. Combined with single-cell sequencing and experimental results, we demonstrated that PD-L2 specifically inhibited the proliferation of CD8+ T cells and the secretion of granzyme B and perforin, leading to reduced apoptosis mediated by CD8+ T cells and enhanced immune escape of tumor cells, which drives EGFR-TKIs resistance. Importantly, we have identified a potent natural small-molecule inhibitor of PD-L2, zinc undecylenate. In vitro, it selectively and potently blocks the PD-L2/PD-1 interaction. In vivo, it abolishes the suppressive effect of the PD-L2-overexpressing tumor immune microenvironment by blocking PD-L2/PD-1 signaling. Moreover, the combination of zinc undecylenate and EGFR-TKIs can synergistically reverse tumor resistance, which is dependent on CD8+ T cells mediating apoptosis. Our study uncovers the PD-L2/PD-1 signaling pathway as a driving factor to mediate EGFR-TKIs resistance, and identifies a new naturally-derived agent to reverse EGFR-TKIs resistance.

Commitment to Changing Services to Be the Best for Championing (Innovative) Behavior Post-Accreditation SNARS Version at Gambiran Regional Hospital, Kediri City

The growth of hospitals creates increasingly fierce competition and customers increasingly have selective choices, and this is a challenge that will affect the sustainability of the organization. Alternative competitive strategies are needed for hospitals to improve the quality and quality of services through accreditation assessments. Post-accreditation results from hospitals alone will not be enough to be balanced with a commitment to change services for the better. Good commitment will give rise to championing behavior in facing increasingly dynamic environmental changes. Research purposes. Analyzing the relationship between commitment to changing services to the best and championing (innovative) behavior after SNARS version accreditation at Gambiran Regional Hospital, Kediri City. Research methods. Quantitative research with correlational design. The population in this study were ASN nurse employees at Gambiran Regional Hospital, Kediri City with a sample of 122 respondents using the Proportional Random Sampling technique. The independent variable is commitment to change and the dependent variable is championing behavior. Data collection used questionnaires and Spearman correlation analysis. Research result. The results of the Spearman correlation test analysis showed that there was no relationship between commitment and Championing (p value 0.569 > 0.05). Conclusion. The innovative behavior of ASN employees is influenced by many factors, not only in terms of organizational commitment

Dynamic changes in evapotranspiration, canopy photosynthesis and expansive growth in open field under rapid fluctuating radiation

AbstractIn open fields, plants experience dynamic changes in environment, particularly radiation, temperature, wind, and humidity but their short-term responses have not been adequately characterized under natural conditions. In this study, we assessed causal effects of rapid radiation fluctuation on seven plant parameters in open fields of cotton and sweet corn canopies. The parameters are evapotranspiration (ET), canopy photosynthesis indicated by CO2 flux from air to canopy (FCO2), sensible heat energy flux (H), canopy conductance for water vapor (gcw), canopy surface temperature (Ts), shoot and leaf elongation rate, and stem diameter change. The energy and CO2 fluxes were measured with Bowen ratio/energy balance/CO2 gradient (BREB+) technique, using averaging time of 5 min. Shoot + leaf elongation and stem diameter change were monitored with position transducers using averaging time of 1 min. All parameters were all found to respond to change in radiation and transpiration within minutes or sooner. While radiation effects on canopy gas exchanges are expected, illuminating are the indirect but immediate effects on shoot + leaf growth and stem diameter change through radiation effects on transpiration and plant water status. A novel finding is that gcw also responded within minutes or sooner to radiation fluctuations and that FCO2 was related almost linearly to gcw. Results are discussed in terms of soil-plant-atmosphere continuum, and interpreted in terms of dynamic interactions between transpiration and plant water status. The clear inverse relationship between ET and elongation rate or stem diameter changes provides additional evidence supporting the validity of 5-min averaging for the BREB + technique.

Seasonal drives on potentially toxic elements dynamics in a tropical estuary impacted by mine tailings

This study investigates the impact of seasonality on estuarine soil geochemistry, focusing on redox-sensitive elements, particularly Fe, in a tropical estuary affected by Fe-rich mine tailings. We analyzed soil samples for variations in particle size, pH, redox potential (Eh), and the content of Fe, Mn, Cr, Cu, Ni, and Pb. Additionally, sequential extraction was employed to understand the fate of these elements. Results revealed dynamic changes in the soil geochemical environment, transitioning between near-neutral and suboxic/anoxic conditions in the wet season and slightly acidic to suboxic/oxic conditions in the dry season. During the wet season, fine particle deposition (83%) rich in Fe (50 g kg−1), primarily comprising crystalline Fe oxides, occurred significantly. Conversely, short-range ordered Fe oxides dominated during the dry season. Over consecutive wet/dry seasons, substantial losses of Fe (−55%), Mn (−41%), and other potentially toxic elements (Cr: −44%, Cu: −31%, Ni: −25%, Pb: −9%) were observed. Despite lower pseudo-total PTE contents, exchangeable PTEs associated with carbonate content increased over time (Cu: +188%, Ni: +557%, Pb: +99%). Modeling indicated climatic variables and short-range oxides substantially influenced PTE bioavailability, emphasizing the ephemeral Fe oxide control during the wet season and heightened ecological and health risks during the dry seasons.

Design and Development of Multi-Agent Reinforcement Learning Intelligence on the Robotarium Platform for Embedded System Applications

This research explores the use of Q-Learning for real-time swarm (Q-RTS) multi-agent reinforcement learning (MARL) algorithm for robotic applications. This study investigates the efficacy of Q-RTS in the reducing convergence time to a satisfactory movement policy through the successful implementation of four and eight trained agents. Q-RTS has been shown to significantly reduce search time in terms of training iterations, from almost a million iterations with one agent to 650,000 iterations with four agents and 500,000 iterations with eight agents. The scalability of the algorithm was addressed by testing it on several agents’ configurations. A central focus was placed on the design of a sophisticated reward function, considering various postures of the agents and their critical role in optimizing the Q-learning algorithm. Additionally, this study delved into the robustness of trained agents, revealing their ability to adapt to dynamic environmental changes. The findings have broad implications for improving the efficiency and adaptability of robotic systems in various applications such as IoT and embedded systems. The algorithm was tested and implemented using the Georgia Tech Robotarium platform, showing its feasibility for the above-mentioned applications.

A Point Cloud Segmentation Method for Pigs from Complex Point Cloud Environments Based on the Improved PointNet++

In animal husbandry applications, segmenting live pigs in complex farming environments faces many challenges, such as when pigs lick railings and defecate within the acquisition environment. The pig’s behavior makes point cloud segmentation more complex because dynamic animal behaviors and environmental changes must be considered. This further requires point cloud segmentation algorithms to improve the feature capture capability. In order to tackle the challenges associated with accurately segmenting point cloud data collected in complex real-world scenarios, such as pig occlusion and posture changes, this study utilizes PointNet++. The SoftPool pooling method is employed to implement a PointNet++ model that can achieve accurate point cloud segmentation for live pigs in complex environments. Firstly, the PointNet++ model is modified to make it more suitable for pigs by adjusting its parameters related to feature extraction and sensory fields. Then, the model’s ability to capture the details of point cloud features is further improved by using SoftPool as the point cloud feature pooling method. Finally, registration, filtering, and extraction are used to preprocess the point clouds before integrating them into a dataset for manual annotation. The improved PointNet++ model’s segmentation ability was validated and redefined with the pig point cloud dataset. Through experiments, it was shown that the improved model has better learning ability across 529 pig point cloud data sets. The optimal mean Intersection over Union (mIoU) was recorded at 96.52% and the accuracy at 98.33%. This study has achieved the automatic segmentation of highly overlapping pigs and pen point clouds. This advancement enables future animal husbandry applications, such as estimating body weight and size based on 3D point clouds.

BN-BacArena: Bayesian network extension of BacArena for the dynamic simulation of microbial communities.

Simulating gut microbial dynamics is extremely challenging. Several computational tools, notably the widely used BacArena, enable modeling of dynamic changes in the microbial environment. These methods, however, do not comprehensively account for microbe-microbe stimulant or inhibitory effects or for nutrient-microbe inhibitory effects, typically observed in different compounds present in the daily diet. Here, we present BN-BacArena, an extension of BacArenaconsisting on the incorporation within the native computational framework of a Bayesian network model that accounts for microbe-microbe and nutrient-microbe interactions. Using in vitro experiments, 16S rRNA gene sequencing data and nutritional composition of 55 foods, the output Bayesian network showed 23 significant nutrient-bacteria interactions, suggesting the importance of compounds such as polyols, ascorbic acid, polyphenols and other phytochemicals, and 40 bacteria-bacteria significant relationships. With test data, BN-BacArena demonstrates a statistically significant improvement over BacArena to predict the time-dependent relative abundance of bacterial species involved in the gut microbiota upon different nutritional interventions. As a result, BN-BacArena opens new avenues for the dynamic modeling and simulation of the human gut microbiota metabolism. MATLAB and R code are available in https://github.com/PlanesLab/BN-BacArena.

Dynamic Changes of Environment and Gut Microbial Community of Litopenaeus vannamei in Greenhouse Farming and Potential Mechanism of Gut Microbial Community Construction

The aim of this study was to investigate the dynamic changes in the microbial communities of both the environment and gut of Litopenaeus vannamei, as well as to elucidate the mechanisms underlying microbial community assembly in greenhouse farming. 16S rDNA high-throughput sequencing and bioinformatics methods were used to carry out the research on the community structure of the microorganisms under greenhouse culture conditions in water, sediment, and gut microorganisms; correlations pertaining to environmental factors; the feasibility of using Source Tracker; and the mechanisms of community construction. The results show that the dominant microorganisms in water, sediment, and gut farming in a greenhouse environment varied and were subject to dynamic change. A variety of beneficial microbiota such as Bacillus were found in the gut, whereas a variety of microorganisms such as Marivita and Pseudomonas, which function as nitrogen and phosphorus removers, were present in water. Source Tracker and environmental correlation analyses showed that changes in the gut were associated with eutrophication indicators (total nitrogen, total phosphorus, ammonia nitrogen) and changes in environmental microorganisms (in water and sediment). The results of the community-building mechanism analysis show that stochastic processes determine the community-building directions of environmental and gut microorganisms. These findings will help us to understand the microbiota characteristics of shrimp ponds under greenhouse farming conditions, and the complex interactions between the shrimp gut and the environmental microbiota and environmental variables, as well as revealing the changing rules of the gut microbiota.

Spatiotemporal use of urban rivers by local bat populations in a large city (Cracow, Southern Poland)

AbstractUrban watercourses are an essential part of blue-green infrastructure (BGI) that can mitigate the negative impact of habitat loss resulting from urbanization. Many studies on temperate urban bats activity did not concern its seasonal variations. Our work evaluated the bats’ activity and occurrence in different urban riverside environments throughout the vegetation period. Our results showed that long-range echolocators were represented mainly by Nyctalus noctula. Their activity peak occurred in late summer in open habitats of high anthropogenic pressure levels, and at least part of the population was present year-round in the city. Recently reported from Cracow: Hypsugo savii and Pipistrellus kuhlii were confirmed in the medium-range echolocators. The high occurrence of P. pygmeus with a relatively low P. pipistrellus was noteworthy and is probably a result of the low competitiveness of the latter in the presence of P. kuhlii. Two spring and late summer activity peaks on different rivers suggest different migration corridors from and to hibernacula. However, the autumn activity in the city center may also indicate partial hibernation in the old town. The activity peak of short-range echolocators occurred in spring, as expected - mainly in a narrow riverbed and low anthropogenic pressure environments. However, ambiguous spring and late summer activity in the open with the highest anthropogenic pressure environment may result from the rapidly progressing synurbization of M. daubentonii. Our results indicate the need to monitor the bat adaptation process to the cities in the context of dynamic climatic and environmental changes.

Spatio-temporal brain invasion pattern of Streptococcus pneumoniae and dynamic changes in the cellular environment in bacteremia-derived meningitis

Streptococcus pneumoniae (the pneumococcus) is the major cause of bacterial meningitis globally, and pneumococcal meningitis is associated with increased risk of long-term neurological sequelae. These include several sensorimotor functions that are controlled by specific brain regions which, during bacterial meningitis, are damaged by a neuroinflammatory response and the deleterious action of bacterial toxins in the brain. However, little is known about the invasion pattern of the pneumococcus into the brain. Using a bacteremia-derived meningitis mouse model, we combined 3D whole brain imaging with brain microdissection to show that all brain regions were equally affected during disease progression, with the presence of pneumococci closely associated to the microvasculature. In the hippocampus, the invasion provoked microglial activation, while the neurogenic niche showed increased proliferation and migration of neuroblasts. Our results indicate that, even before the outbreak of symptoms, the bacterial load throughout the brain is high and causes neuroinflammation and cell death, a pathological scenario which ultimately leads to a failing regeneration of new neurons.

Organizational and legal ensuring the administrative activities of managers

This paper is devoted to a thorough analysis of the legal basis for ensuring management activities in the conditions of modern Ukrainian legislation. The authors note that in the conditions of dynamic changes in the business environment, legal protection acquires strategic importance for making informed management decisions, minimizing risks and ensuring stable development of the organization. The administrative and legal status of the manager in Ukraine is analyzed in detail - his role and powers in the management system, subordination, implementation of the principle of single leadership, the scope of competence and legal responsibility. The main principles of legal support of the manager's managerial activity were considered. Among them, the principles of legality, responsibility, transparency, ethics, as well as mechanisms of internal and external legal regulation are analyzed in detail. Examples of its practical application are provided for each principle. The authors also pay attention to the influence of key branches of law on managerial activity. These are corporate, labor, tax, antimonopoly legislation, laws on the protection of intellectual property, on the protection of consumer rights, etc. The conclusions emphasize that in modern conditions management of the organization is impossible without comprehensive legal support. It makes it possible to make effective management decisions, avoid legal risks, ensure stability and development of activities. The paper examines the administrative and legal status of the manager, the principles and components of legal support for his activity from the point of view of modern Ukrainian legislation. A reasonable conclusion is made about the key importance of legal aspects for effective management.

PSO Training Neural Network MPPT with CUK Converter Topology for Stand-Alone PV Systems Under Varying Load and Climatic Conditions

Temperature and irradiance levels are two examples of environmental variables that affect the power value produced by photovoltaic panels. Therefore, in order to transfer the maximum power value from the PV panel to the load under varying climatic conditions, maximum power point tracking (MPPT) algorithms and DC-DC converter topologies are used. In this study, the performances of boost converter and CUK converter circuit topologies are investigated under variable irradiance and variable load conditions by using a neural network-based MPPT algorithm learning particle swarm optimization (PSO). As the first scenario, it is analyzed assuming that the temperature and irradiance values coming to the panel are constant. As the second scenario, the performance evaluation of the converter topologies according to the current, voltage and power parameters is made for the variable load situation. As the last scenario, the difference in the irradiance value coming to the panel depending on the sun's condition during the day has been examined. Canadian Solar CS6P-250P PV panel is used in the study. 50 kHz is selected as the switching frequency. According to the results obtained, it has been observed that the CUK converter circuit topology reaches the maximum power point faster than the boost converter circuit topology both in dynamic environmental conditions and load change, and the oscillation at this point is less. It is aimed to increase the performance of this method, which uses boost converter topology and MPPT in the literature, by applying CUK converter topology.

Deep Reinforcement Learning Car-Following Control Based on Multivehicle Motion Prediction

Reinforcement learning (RL)–based car-following (CF) control strategies have attracted significant attention in academia, emerging as a prominent research topic in recent years. Most of these control strategies focus solely on the motion status of the immediately preceding vehicle. However, with the development of vehicle-to-vehicle (V2V) communication technologies, intelligent vehicles such as connected autonomous vehicles (CAVs) can gather information about surrounding vehicles. Therefore, this study proposes an RL-based CF control strategy that takes multivehicle scenarios into account. First, the trajectories of two preceding vehicles and one following vehicle relative to the subject vehicle (SV) are extracted from a highD dataset to construct the environment. Then the twin-delayed deep deterministic policy gradient (TD3) algorithm is implemented as the control strategy for the agent. Furthermore, a sequence-to-sequence (seq2seq) module is developed to predict the uncertain motion statuses of surrounding vehicles. Once integrated into the RL framework, this module enables the agent to account for dynamic changes in the traffic environment, enhancing its robustness. Finally, the performance of the CF control strategy is validated both in the highD dataset and in two traffic perturbation scenarios. In the highD dataset, the TD3-based prediction CF control strategy outperforms standard RL algorithms in terms of convergence speed and rewards. Its performance also surpasses that of human drivers in safety, efficiency, comfort, and fuel consumption. In traffic perturbation scenarios, the performance of the proposed CF control strategy is compared with the model predictive controller (MPC). The results show that the TD3-based prediction CF control strategy effectively mitigates undesired traffic waves caused by the perturbations from the head vehicle. Simultaneously, it maintains the desired traffic state and consistently ensures a stable and efficient traffic flow.

Rama’s Orange Peel Emotional Intelligence

Purpose: We homosapians are natural to emotions. We all have different personalities, different wants, needs, and different ways of showing emotion. Yes, it is a natural tendency of every individual to express, show, or to have emotions. Today organizations for a long period who believe that the success of a person at the workplace depends only on the level of intelligence is a false statement, instead, organizations believe that they need a very important soft skill called emotional intelligence to achieve organizational goals. This research paper is a thought-provoking blend of management, science, and mythology. The researcher elucidates how Rama’s EQ and his positive psychology explain EI. The outcome of the research assists Rama’s orange peel emotional intelligence for individual personalities to discover them as well as to understand insights into how to navigate challenging situations & emotions in everyday life & workplace. Objectives: The researchers have established the undermentioned primary objectives and the present study investigates 3 broad research questions: (1) To examine the positive psychology of Rama. (2) To evaluate Rama’s contemporary principles of emotional intelligence. (3) To identify Rama’s orange peel emotional intelligence. Methodology: The study is based on both secondary & primary evidence. Hypotheses are stated for the various variables selected and scrutinized to prove or disprove the same without any errors. Findings/Result: The study found Ramayana with many characters mirrors the range of human emotions & correlates positive psychology, and contemporary principles of EI. Modern organizations are highly evaporating, and dynamic and demand higher productivity. Tasks cannot simply be accomplished individually or by working with others in fixed ways. For all these, the one and only answer to cope with the pressure cooker situations that are the reality of the workplace is a right mixture of head and heart (Emotional Intelligence) & the new wellness trend Rama’s orange peel emotional intelligence. Research Limitations: Innovative technologies and rapid changes in the business environment have created corporate managers undesirable traits like anxiety, fear, worries, stress, and strain in the lifestyles of individuals. Opening the doors of Head & Heart (Emotional Intelligence) through Rama’s orange peel emotional intelligence can reframe and transform the limitations. In the current research, an attempt is made by the researchers to comprehend the research gap in the area hence a conceptual idea is proposed and the research goes further to advance significance to discover the degree of relationship to interpret characters from Ramayana to explain Rama’s orange peel emotional intelligence. Originality/Value: So with compelling changes in the present dynamic, scientific, and business environment the paper throws light on what the corporate world looks like from managers is a new strategies that are followed at the workplace and different yardsticks that are used to judge people because they not only judge how smart you are, what is your experience or what is your educational qualification but also how well you are able to tackle yourself and others through Rama’s orange peel emotional intelligence. Paper Type: The study is based on both secondary & primary evidence. There are many characters in the Ramayana, with each one having good and bad qualities. Many characters are alike in intelligence but differ in their ethical characteristics. Hence, researchers have put their effort into giving intelligence ratings as well as ethical ratings to the major characters in the research. However, this is not a definitive list.

Reliable sleep temperature regulation based on PMV model and dynamic thermal parameters

Quality of nighttime sleep is crucial. The common practice of air conditioners' sleep mode is to recommend a fixed sleep curve or suggest a pre-trained temperature curve based on geologic location. However, these models neglect the user preference and dynamic changes in the room's thermal environment, making it difficult to satisfy people of different ages and genders and resulting in discomfort during sleep. Demanding user to input their preferences or private data causes privacy issues, which leads to a bottleneck in solving this problem. Therefore, the paper proposes a new dynamic thermal parameter model for sleep, which learns the users' metabolic rate and thermal resistance of clothing and bedding through the user's undetected setting before sleep and simple feedback after waking up. These parameters fully reflect the inherent preferences of users and the thermal environment of rooms. After 1–2 rounds of feedback, the model can simulate the needs of users pretty well. In addition, we have optimized the PMV sleep model based on the dynamic thermal parameter model to regulate the temperature of air conditioner. In the controlled experiment, 84% of the participants expressed satisfaction with the regulated sleep temperature environment using the new model, which is 17% higher than that of the fixed curve. This model establishes a personalized model for users without obtaining their privacy information. In future research, wind speed control and humidity control can be introduced, and other sleep monitoring devices can be added to achieve more precise air conditioning temperature control.