Concept of Net Zero Ecotourism in Sustainable Tourism Industry Development

With the increase of demand and popularity of ecotourism development to address the concern of carbon emission, the acceleration of development of the concept of net zero carbon ecotourism can increase international competitiveness, sustainability and productivity. This paper aims to outline the major key components and considerations in ecotourism development with integration of net zero strategy and provide recommendation and reference to government agents, Architecture, Engineering and Construction (AEC) industry and stakeholders to contribute to the target of net zero and environmentally friendly ecotourism development project. This paper explores the alternative to the reliance on local regulation and ecotourism certification programs as a base tool to achieve the higher standard of the reduction of the use of energy and natural resources in ecotourism development and to enhance their sustainability.

The Use of Artificial Intelligence in Digital Forensics and Incident Response in a Constrained Environment

Digital investigators often have a hard time spotting evidence in digital information. It has become hard to determine which source of proof relates to a specific investigation. A growing concern is that the various processes, technology, and specific procedures used in the digital investigation are not keeping up with criminal developments. Therefore, criminals are taking advantage of these weaknesses to commit further crimes. In digital forensics investigations, artificial intelligence (AI) is invaluable in identifying crime. Providing objective data and conducting an assessment is the goal of digital forensics and digital investigation, which will assist in developing a plausible theory that can be presented as evidence in court. This research paper aims at developing a multiagent framework for digital investigations using specific intelligent software agents (ISAs). The agents communicate to address particular tasks jointly and keep the same objectives in mind during each task. The rules and knowledge contained within each agent are dependent on the investigation type. A criminal investigation is classified quickly and efficiently using the case-based reasoning (CBR) technique. The proposed framework development is implemented using the Java Agent Development Framework, Eclipse, Postgres repository, and a rule engine for agent reasoning. The proposed framework was tested using the Lone Wolf image files and datasets. Experiments were conducted using various sets of ISAs and VMs. There was a significant reduction in the time taken for the Hash Set Agent to execute. As a result of loading the agents, 5% of the time was lost, as the File Path Agent prescribed deleting 1,510, while the Timeline Agent found multiple executable files. In comparison, the integrity check carried out on the Lone Wolf image file using a digital forensic tool kit took approximately 48 minutes (2,880 ms), whereas the MADIK framework accomplished this in 16 minutes (960 ms). The framework is integrated with Python, allowing for further integration of other digital forensic tools, such as AccessData Forensic Toolkit (FTK), Wireshark, Volatility, and Scapy.

Embedded Electrochemistry with a Miniaturized, Drone-Based, Potentiostat System for Remote Detection Chemical Warfare Agents

The development of an embedded miniaturized drone-based system for remote detection of Chemical Warfare Agents (CWAs) is proposed. The paper focuses on the software/hardware system design of the electrochemical Cyclic Voltammetry (CV) and Differential Pulse Voltammetry (DPV) signal processing for future deployment on drones. The paper summarizes the progress made towards hardware and electrochemical signal processing for signature detection of CWA. Also, the miniature potentiostat signal is validated by comparing it with the high-end lab potentiostat signal.

Performance Evaluation and Plugging Characteristics of Controllable Self-Aggregating Colloidal Particle Profile Control Agent

In low permeability reservoirs, the reservoir pore throat is small and the micro heterogeneity is prominent. Conventional microsphere profile control agents generally have good injectability but poor plugging effect; however, profile control agents with good plugging effect generally have poor injectability, which makes it difficult for agent to realize deep profile control of reservoir. To solve this problem, styrene and acrylamide were used as monomers in the laboratory. Emulsion polymerization was used to prepare the Controllable Self-Aggregating Colloidal Particle (CSA), which was rich in amide group. The CSA microsphere dispersion solution with a particle diameter smaller than the pore throat diameter was injected into the reservoir to ensure that the profile control agent had good inject ability. After dispersing the CSA microsphere to the deep part of the reservoir, the CSA microspheres dispersed in static for a certain period of time will self-aggregate into large-sized particle clusters to achieve plugging of hypertonic channels. The CSA microsphere has the characteristics of low expansion and avoids shear fracture in the process of migration. It can be observed by transmission electron microscope that CSA microspheres still maintain regular and uniform spherical and core-shell heterogeneous structure after aging at 100 ºC for 35 days, and CSA microspheres have good thermal stability. The results of bottle test showed that with the increase of cation concentration, the aggregation time of CSA microspheres gradually shortened, and the influence of divalent cations was greater than that of monovalent ions. Physical simulation experiments show that CSA microspheres have good injectability, and the aggregated CSA particle clusters can produce effective plugging and migrate to the deep part of the reservoir for profile control.

Influence of Laser Treatment on the Growth of Sprouts of Different Wheat Varieties

Cereals are considered as a strategic product in human life and their demand is increasing with the growth of world population. Increasing wheat production is important for the country. One of the ways to solve the problem is to develop and implement new, environmentally and economically acceptable technologies. Such technologies include pre-sowing treatment of seed with a laser and associative nitrogen-fixing bacteria - Azospirillum brasilense. In the region there are the wheat varieties - Dika and Lomtagora, which are among the most common in Georgia. Dika is a frost-resistant wheat, with a high ability to adapt to the environment, resistant to falling and it is sown in highlands. Lomtagora 126 differs with its winter and drought resistance, and it has a great ability to germinate. Lomtagora is characterized by a strong root system and a high budding capacity. It is an early variety, fall-resistant, easy to thresh and suitable for mechanized harvesting with large and red grains. This paper presents some preliminary experimental results where a continuous CO2 laser with a power of 25-40 W was used to radiate grains at a flow rate of 10 and 15 cm/sec. The treatment was carried out on grains of the Triticum aestivum L. var. Lutescens (local variety name - Lomtagora 126), and Triticum carthlicum Nevski (local variety name - Dika). Here the grains were treated with A. brasilense isolate (108-109 CFU/ml), which was isolated from the rhizosphere of wheat. It was observed that the germination of the wheat was not significantly influenced by either laser or bacteria treatment. The results of our research show that combined treatment with laser and A. brasilense significantly influenced the germination of wheat. In the case of the Lomtagora 126 variety, grains were exposed to the beam on a speed of 10 cm/sec, only slightly improved the growth for 38-day seedlings, in case of exposition of grains with a speed of 15 cm/sec - by 23%. Treatment of seeds with A. brasilense in both exposed and non-exposed variants led to an improvement in the growth of seedlings, with A. brasilense alone - by 22%, and with combined treatment of grains - by 29%. In the case of the Dika variety, only exposure led to growth by 8-9%, and the combined treatment - by 10-15%, in comparison with the control variant. Superior effect on growth of seedlings of different varieties was achieved with the combinations of laser treatment on grains in a beam of 15 cm/sec (radiation power 30-40 W) and in addition of A. brasilense - nitrogen fixing bacteria. Therefore, this is a promising application of A. brasilense as active agents of bacterial fertilizers due to their ability of molecular nitrogen fixation in cereals in combination with laser irradiation: choosing a proper strain gives a good ability to colonize roots of agricultural crops, providing a high nitrogen-fixing ability and the ability to mobilize soil phosphorus, and laser treatment stimulates natural processes occurring in plant cells, will increase the yield.

Parameters Influencing Human-Machine Interaction in Hospitals

Handling life-critical systems complexity requires to be equipped with appropriate technology and the right human agents’ functions such as knowledge, experience, and competence in problem’s prevention and solving. Human agents are involved in the management and control of human-machine system’s performance. Documenting human agent’s situation awareness is crucial to support human-machine designers’ decision-making. Knowledge about risks, critical parameters and factors that can impact and threaten automation system’s performance should be collected using preventive and retrospective approaches. This paper aims to document operators’ situation awareness through the analysis of automated organizations’ feedback. The analysis of automated hospital pharmacies feedback helps identify and control critical parameters influencing human machine interaction in order to enhance system’s performance and security. Our human machine system evaluation approach has been deployed in Macon hospital center’s pharmacy which is equipped with automated drug dispensing systems since 2015. Automation’s specifications are related to technical aspects, human-machine interaction, and human aspects. The evaluation of drug delivery automation performance in Macon hospital center has shown that the performance of the automated activity depends on the performance of the automated solution chosen, and also on the control of systemic factors. In fact, 80.95% of automation specification related to the chosen Sinteco’s automated solution is met. The performance of the chosen automated solution is involved in 28.38% of automation specifications performance in Macon hospital center. The remaining systemic parameters involved in automation specifications performance need to be controlled. 

Simulation of Obstacle Avoidance for Multiple Autonomous Vehicles in a Dynamic Environment Using Q-Learning

The availability of inexpensive, yet competent hardware allows for increased level of automation and self-optimization in the context of Industry 4.0. However, such agents require high quality information about their surroundings along with a robust strategy for collision avoidance, as they may cause expensive damage to equipment or other agents otherwise. Manually defining a strategy to cover all possibilities is both time-consuming and counter-productive given the capabilities of modern hardware. This paper explores the idea of a model-free self-optimizing obstacle avoidance strategy for multiple autonomous agents in a simulated dynamic environment using the Q-learning algorithm.

The Effects of Cross-Border Use of Drones in Nigerian National Security

Drone technology has become a significant discourse in a nation’s national security, while this technology could constitute a danger to national security on the one hand, on the other hand, it is used in developed and developing countries for border security, and in some cases, for protection of security agents and migrants. In the case of Nigeria, drones are used by the military to monitor and tighten security around the borders. However, terrorist groups have devised a means to utilize the technology to their advantage. Therefore, the potential danger in the widespread proliferation of this technology has become a myriad of risks. The research on the effects of cross-border use of drones in Nigerian national security looks at the negative and positive consequences of using drone technology. The study employs the use of interviews and relevant documents to obtain data while the study applied the Just War theory to justify the reason why countries use force; it further buttresses the points with what the realist theory thinks about the use of force. In conclusion, the paper recommends that the Nigerian government through the National Assembly should pass a bill for the establishment of a law that will guide the use of armed and unarmed drones in Nigeria enforced by the Nigeria Civil Aviation Authority and the office of the National Security Adviser.

Green, Smooth and Easy Electrochemical Synthesis of N-Protected Indole Derivatives

Here, we report a simple method for the direct conversion of 6-Nitro-1H-indole into N-substituted indoles via electrochemical dehydrogenative reaction with halogenated reagents under strongly basic conditions through N–R bond formation. The N-protected indoles have been prepared under moderate and scalable electrolytic conditions. The conduct of the reactions was performed in a simple divided cell under constant current without oxidizing reagents or transition-metal catalysts. The synthesized products have been characterized via UV/Vis spectrophotometry, 1H-NMR, and FTIR spectroscopy. A possible reaction mechanism is discussed based on the N-protective products. This methodology could be applied to the synthesis of various biologically active N-substituted indole derivatives.

AI Tutor: A Computer Science Domain Knowledge Graph-Based QA System on JADE platform

In this paper, we proposed an AI Tutor using ontology and natural language process techniques to generate a computer science domain knowledge graph and answer users’ questions based on the knowledge graph. We define eight types of relation to extract relationships between entities according to the computer science domain text. The AI tutor is separated into two agents: learning agent and Question-Answer (QA) agent and developed on JADE (a multi-agent system) platform. The learning agent is responsible for reading text to extract information and generate a corresponding knowledge graph by defined patterns. The QA agent can understand the users’ questions and answer humans’ questions based on the knowledge graph generated by the learning agent.

Probabilistic Approach of Dealing with Uncertainties in Distributed Constraint Optimization Problems and Situation Awareness for Multi-agent Systems

In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging.

Multi-Agent Searching Adaptation Using Levy Flight and Inferential Reasoning

In this paper, we describe how to achieve knowledge understanding and prediction (Situation Awareness (SA)) for multiple-agents conducting searching activity using Bayesian inferential reasoning and learning. Bayesian Belief Network was used to monitor agents' knowledge about their environment, and cases are recorded for the network training using expectation-maximisation or gradient descent algorithm. The well trained network will be used for decision making and environmental situation prediction. Forest fire searching by multiple UAVs was the use case. UAVs are tasked to explore a forest and find a fire for urgent actions by the fire wardens. The paper focused on two problems: (i) effective agents’ path planning strategy and (ii) knowledge understanding and prediction (SA). The path planning problem by inspiring animal mode of foraging using Lévy distribution augmented with Bayesian reasoning was fully described in this paper. Results proof that the Lévy flight strategy performs better than the previous fixed-pattern (e.g., parallel sweeps) approaches in terms of energy and time utilisation. We also introduced a waypoint assessment strategy called k-previous waypoints assessment. It improves the performance of the ordinary levy flight by saving agent’s resources and mission time through redundant search avoidance. The agents (UAVs) are to report their mission knowledge at the central server for interpretation and prediction purposes. Bayesian reasoning and learning were used for the SA and results proof effectiveness in different environments scenario in terms of prediction and effective knowledge representation. The prediction accuracy was measured using learning error rate, logarithm loss, and Brier score and the result proves that little agents mission that can be used for prediction within the same or different environment. Finally, we described a situation-based knowledge visualization and prediction technique for heterogeneous multi-UAV mission. While this paper proves linkage of Bayesian reasoning and learning with SA and effective searching strategy, future works is focusing on simplifying the architecture.

Inferential Reasoning for Heterogeneous Multi-Agent Mission

We describe issues bedeviling the coordination of heterogeneous (different sensors carrying agents) multi-agent missions such as belief conflict, situation reasoning, etc. We applied Bayesian and agents' presumptions inferential reasoning to solve the outlined issues with the heterogeneous multi-agent belief variation and situational-base reasoning. Bayesian Belief Network (BBN) was used in modeling the agents' belief conflict due to sensor variations. Simulation experiments were designed, and cases from agents’ missions were used in training the BBN using gradient descent and expectation-maximization algorithms. The output network is a well-trained BBN for making inferences for both agents and human experts. We claim that the Bayesian learning algorithm prediction capacity improves by the number of training data and argue that it enhances multi-agents robustness and solve agents’ sensor conflicts.

A Constructivist Approach and Tool for Autonomous Agent Bottom-up Sequential Learning

During the initial phase of cognitive development, infants exhibit amazing abilities to generate novel behaviors in unfamiliar situations, and explore actively to learn the best while lacking extrinsic rewards from the environment. These abilities set them apart from even the most advanced autonomous robots. This work seeks to contribute to understand and replicate some of these abilities. We propose the Bottom-up hiErarchical sequential Learning algorithm with Constructivist pAradigm (BEL-CA) to design agents capable of learning autonomously and continuously through interactions. The algorithm implements no assumption about the semantics of input and output data. It does not rely upon a model of the world given a priori in the form of a set of states and transitions as well. Besides, we propose a toolkit to analyze the learning process at run time called GAIT (Generating and Analyzing Interaction Traces). We use GAIT to report and explain the detailed learning process and the structured behaviors that the agent has learned on each decision making. We report an experiment in which the agent learned to successfully interact with its environment and to avoid unfavorable interactions using regularities discovered through interaction.

The Alliance for Grassland Renewal: A Model for Teaching Endophyte Technology

To the author’s best knowledge, there are no published reports of effective methods for teaching fescue toxicosis and grass endophyte technology in the USA. To address this need, a group of university scientists, industry representatives, government agents, and livestock producers formed an organization called the Alliance for Grassland Renewal. One goal of the Alliance was to develop a teaching method that could be employed across all regions in the USA and all sectors of the agricultural community. The first step in developing this method was identification of experts who were familiar with the science and management of fescue toxicosis. The second step was curriculum development. Experts wrote a curriculum that addressed all aspects of toxicosis and management, including toxicology, animal nutrition, pasture management, economics, and mycology. The curriculum was created for presentation in lectures, laboratories, and in the field. The curriculum was in that it could be delivered across state lines, regardless of peculiar, in-state recommendations. The curriculum was also unique as it was unanimously supported by private companies otherwise in competition with each other. The final step in developing this teaching method was formulating a delivery plan. All experts, including university, industry, government, and production, volunteered to travel from any state in the USA, converge in one location, teach a 1-day workshop, then travel to the next location. The results of this teaching method indicate widespread success. Since 2012, experts across the entire USA have converged to teach Alliance workshops in Kansas, Oklahoma, Missouri, Kentucky, Georgia, South Carolina, North Carolina, and Virginia, with ongoing workshops in Arkansas and Tennessee. Data from post-workshop surveys indicate that instruction has been effective, as at least 50% of the participants stated their intention to adopt the endophyte technology presented in these workshops. The teaching method developed by the Alliance for Grassland Renewal has proved to be effective, and the Alliance continues to expand across the USA.

Personal Factors and Career Adaptability in a Call Centre Work Environment: The Mediating Effects of Professional Efficacy

The study discussed in this article sought to assess whether a sense of professional efficacy mediates the relationship between personal factors and career adaptability. A quantitative cross-sectional survey approach was followed. A non–probability sample of (N = 409) of which predominantly early career and permanently employed black females in call centres in Africa participated in this study. In order to assess personal factors, the participants completed sense of meaningfulness and emotional intelligence measures. Measures of professional efficacy and career adaptability were also completed. The results of the mediational analysis revealed that professional efficacy significantly mediates the meaningfulness (sense of coherence) and career adaptability relationship, but not the emotional intelligence–career adaptability relationship. Call centre agents with professional efficacy are likely to be more work engaged as a result of their sense of meaningfulness and emotional intelligence.

Liquid Chromatography Microfluidics for Detection and Quantification of Urine Albumin Using Linear Regression Method

Nearly a hundred per million of the Filipino population is diagnosed with Chronic Kidney Disease (CKD). The early stage of CKD has no symptoms and can only be discovered once the patient undergoes urinalysis. Over the years, different methods were discovered and used for the quantification of the urinary albumin such as the immunochemical assays where most of these methods require large machinery that has a high cost in maintenance and resources, and a dipstick test which is yet to be proven and is still debated as a reliable method in detecting early stages of microalbuminuria. This research study involves the use of the liquid chromatography concept in microfluidic instruments with biosensor as a means of separation and detection respectively, and linear regression to quantify human urinary albumin. The researchers’ main objective was to create a miniature system that quantifies and detect patients’ urinary albumin while reducing the amount of volume used per five test samples. For this study, 30 urine samples of unknown albumin concentrations were tested using VITROS Analyzer and the microfluidic system for comparison. Based on the data shared by both methods, the actual vs. predicted regression were able to create a positive linear relationship with an R2 of 0.9995 and a linear equation of y = 1.09x + 0.07, indicating that the predicted values and actual values are approximately equal. Furthermore, the microfluidic instrument uses 75% less in total volume – sample and reagents combined, compared to the VITROS Analyzer per five test samples.

Advanced Materials Based on Ethylene-Propylene-Diene Terpolymers and Organically Modified Montmorillonite

This paper presents studies on the development and characterization of nanocomposites based on ethylene-propylene terpolymer rubber (EPDM), chlorobutyl rubber (IIR-Cl) and organically modified montmorillonite (OMMT). Mixtures were made containing 0, 3 and 6 phr (parts per 100 parts rubber) OMMT, respectively. They were obtained by melt intercalation in an internal mixer - Plasti-Corder Brabender, in suitable blending parameters, at high temperature for 11 minutes. Curing agents were embedded on a laboratory roller at 70-100 ºC, friction 1:1.1, processing time 5 minutes. Rubber specimens were obtained by compression, using a hydraulic press at 165 ºC and a pressing force of 300 kN. Curing time, determined using the Monsanto rheometer, decreases with the increased amount of OMMT in the mixtures. At the same time, it was noticed that mixtures containing OMMT show improvement in physical-mechanical properties. These types of nanocomposites may be used to obtain rubber seals for the space application or for other areas of application.

Corrosion Protection of Structural Steel by Surfactant Containing Reagents

The anti-corrosion performance of fatty acid coated mild steel samples is studied. Samples of structural steel coated with collector reagents deposited from surfactant in ethanol solution and overcoated with an epoxy barrier paint. A quantitative corrosion rate was determined by linear polarization resistance method using biopotentiostat/galvanostat 400. Coating morphology was determined by scanning electronic microscopy. A test for hydrophobic surface of steel by surfactant was done. From the samples, the main component or high content iron was determined by chemical method and other metal contents were determined by Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES) method. Prior to measuring the corrosion rate, mechanical and chemical treatments were performed to prepare the test specimens. Overcoating the metal samples with epoxy barrier paint after exposing them with surfactant the corrosion rate can be inhibited by 34-35 µm/year.

Dependability Tools in Multi-Agent Support for Failures Analysis of Computer Networks

During their activity, all systems must be operational without failures and in this context, the dependability concept is essential avoiding disruption of their function. As computer networks are systems with the same requirements of dependability, this article deals with an analysis of failures for a computer network. The proposed approach integrates specific tools of the plat-form KB3, usually applied in dependability studies of industrial systems. The methodology is supported by a multi-agent system formed by six agents grouped in three meta agents, dealing with two levels. The first level concerns a modeling step through a conceptual agent and a generating agent. The conceptual agent is dedicated to the building of the knowledge base from the system specifications written in the FIGARO language. The generating agent allows producing automatically both the structural model and a dependability model of the system. The second level, the simulation, shows the effects of the failures of the system through a simulation agent. The approach validation is obtained by its application on a specific computer network, giving an analysis of failures through their effects for the considered network.