Operational Environment Research Articles

Leveraging Learning in Operational Environments: Lessons Learned from a Multinational Experience aboard USS George Washington during the Southern Seas 2024 Mission

Professional military education (PME) serves to prepare individuals and teams to promote peace, deter aggression, lead in times of crisis, and prepare to adapt with purpose in volatile, uncertain, complex, and ambiguous circumstances. Contemporary geopolitical conditions require global leaders to reinforce relations with allies and partners. PME is a platform on which multinational relationships and interoperability can develop and be strengthened. This article explores the roles of human factors in effective PME among a multinational cohort of military officers learning in an operational environment during the Southern Seas 2024 mission to advance partnerships and interoperability. Outcomes reported describe adult development and education methods that foster learning while attending to human factors that improve leader and team development among mid-career professional military officers in operational environments—the exact real-world environment in which PME graduates need to be prepared to excel, collaborate, and lead to win.

The autonomous urban passenger ferry milliAmpere2: Design and testing

Abstract Autonomous urban passenger ferries have the potential to enhance urban mobility. However, despite advances in recent years, operating Autonomous Surface Vehicles (ASVs) in urban waterways remains challenging, not least because transporting passengers raises safety standards to the fore. This paper presents the autonomous urban passenger ferry “milliAmpere2,” developed by the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway. Design features and test results are presented, covering the investigation of five research questions related to human-centered design, batteries and propulsion, autonomous navigation and control, remote monitoring and control, and risk assessment. A three-week public trial of the “milliAmpere2,” held in 2022, helped to synthesize research outcomes in the context of its operational environment in a confined and well-trafficked urban waterway. The “milliAmpere2” project adds to a growing number of use cases demonstrating the viability of ASVs for passenger transportation. Outstanding challenges for future research are identified, including human-autonomy teaming, remote management of fleet operation, and interactions with passengers, traffic, and remote operators.

The Logistics Cost of Cikarang Dry Port: A General Review

High logistics costs in Indonesia are thus one of the principal economic inefficiencies that restrain the country's growth and competitiveness in the global marketplace. In this context, the paper focuses on the contribution of the Cikarang Dry Port (CDP) to the national economy. This qualitative research draws on an extended narrative review of the Cikarang Dry Port. These results reflect how the CDP is pivotal in reducing logistics costs, benefiting clients and manufacturing firms around the dry port area through bettered transportation and logistics service provision. The study also puts much emphasis on the fact that logistics efficiency is not only crucial at the level of individual enterprise performance but also, more broadly, for Indonesia's economic landscape. The CDP consolidates supply chains and reduces transportation costs, ultimately building overall cost competitiveness for Indonesian enterprises in more state and international markets. The study calls for the Indonesian government to invest in developing and upgrading the infrastructure of the Cikarang and Bekasi areas. These strategic investments would also help reduce the overall cost of logistics, thereby creating an enabling environment for business operations and playing a role in the economic development and growth of the nation.

Enhancing Energy Efficiency & Productivity With Solar Cells For Wifi Operations In Mining Are-as Pt Antareja Mahada Makmur Jobsite Mifa Ber-saudara (2024)

This improvement aims to evaluate the effectiveness of the use of solar cell systems in improving energy efficiency and WiFi network productivity in coal mining environments. The research method involves the installation of solar cell systems and the measurement of energy performance and productivity before and after implementation. The implementation of solar cell systems on WiFi networks in mining environments can improve energy efficiency and productivity. This study aims to analyze the influence of the implementation of solar cell systems on fossil energy use and environmental impact. The results show that the implementation of the solar cell system can reduce the use of fossil energy by 75% and reduce greenhouse gas emissions by 60%. The implementation of the solar cell system can also increase productivity by 25%. Therefore, the implementation of a solar cell system on a WiFi network in a mining operational environment is highly recommended. The results showed a significant improvement in the energy efficiency and stability of the WiFi network, which had a positive impact on operational productivity. The results of the study show that the use of solar cells can significantly reduce the consumption of electricity/fossil energy, as well as increase the availability of WiFi networks to support PT AMM's operational support activities. The implication of this study is the great potential of the use of solar energy to improve operational efficiency and reduce environmental impact.

The Impact of Climate Change on Future Ground Operations

The information about battlefield environment changes and operational impacts on Korean Peninsula due to climate change is essential for national defense. In this study, the future impact of four ground operations was analyzed by using the national climate change standard scenario based on the IPCC 6<sup>th</sup> report. As a result, it was analyzed that the number of operational-limited days for ambush and airlift operations would decrease, making the operational environment favorable. However, the operational environment unfavorable as the number of operational-limited days for crossing and reconnaissance operations increase, but the number is not large so much.

AI-Driven Optimization of Last-Mile Delivery

The optimization of last-mile delivery represents a critical challenge in modern e-commerce logistics, consuming a substantial portion of total shipping costs. This comprehensive technical article examines how artificial intelligence and machine learning technologies are revolutionizing last-mile delivery operations through advanced route optimization, demand forecasting, and resource allocation. This article synthesizes findings from recent implementations across major logistics providers, demonstrating that AI-driven route optimization systems significantly reduce delivery times and decrease fuel consumption across diverse operational environments. Analysis of machine learning models deployed by leading e-commerce platforms shows marked improvements in delivery time prediction accuracy, achieving unprecedented precision in estimated delivery windows. This article examines key technical components, including dynamic routing algorithms, predictive demand modeling, and real-time fleet management systems, while also addressing implementation challenges such as data quality and system integration. Case studies from major logistics providers demonstrate substantial ROI improvements following AI implementation, with particular emphasis on neural network architectures optimized for urban delivery scenarios. This article provides a technical framework for understanding how AI technologies transform last-mile logistics while offering insights into future developments, including autonomous delivery systems and smart city integration.

Välkky's voyage on to a hospital ward: Expectations, explorations and emergent robocentric nursing care.

Drawing on ethnographic fieldwork in Finland, we report on the trial of a teleoperated care robot named Välkky introduced onto a fully operational hospital neurological ward. Our data revealed a narrative arc where participants' early expectations of the hospital-based trial altered as the project unfolded. Greeted with techno-excitement and experimental enthusiasm about the place of robotics in reshaping roles within clinical care, Välkky became the focus for collaborative in situ learning, adaptation and redesign amongst the roboticists, designers, nurses, patients, and managers. Välkky acted as an 'attractor' provoking thinking about, and a reimagining of, future arrangements of care. Our empirically informed insights seek to pave the way for real-world nuanced thinking that pushes beyond human/non-human and success/failure binaries. Building on debates in STS and feminist posthumanism, we propose a robocentric approach, which encourages us to 'queer' health care robots, and to understand them as fluid, hybrid, distributed and relational figures, rather than purely as inert, mechanical, non-human objects that might replace humans. Nursing care practices by and with robots will generate new meanings and practices of care that will emerge iteratively, as caring relations, relationships and practices develop within the context of operational ward environments. Robots may or may not be able support care, but they will invariably challenge what care is.

Examining the Role of Organizational Culture in Shaping Security Practices: A Case Study of Tertiary Institutions

This study investigates the critical role of the human factor in enhancing organizational cybersecurity resilience, particularly within the context of tertiary institutions in Nigeria. As organizations increasingly depend on digital technologies, understanding how employee knowledge, behavior, and awareness impact information security is paramount. The research highlights the significant contribution of human error to security breaches, underscoring that even the most sophisticated technological defenses are vulnerable when individuals do not adhere to security protocols. Utilizing a comprehensive literature review, the study examines the implications of human behavior, including negligence and social engineering, on organizational security outcomes. Key findings indicate that insufficient training, lack of supervision, and poor understanding of security policies exacerbate vulnerabilities. Recommendations include implementing robust security awareness programs and ensuring that only qualified personnel teach keyboarding skills, as these practices can mitigate risks. Ultimately, the research advocates for a socio-technical approach to cybersecurity, emphasizing the need for collaboration between technical solutions and human factors to foster a more secure operational environment in the educational sector.

SME in Albania: A Descriptive Analyses of Business Environment Based on SME Policy Index

Micro, small and medium enterprises make an important contribution to the economic development of Albania. Due to globalization and EU integration process, business environment in our country is becoming more and more complex and SMEs have to undertake proper actions in order to compete at national and international markets. Under these circumstances, more attention should be paid to innovation as the main tool to remain competitive in a globalized world. This paper aims to provide a descriptive analysis of SMEs sector in Albania focusing on their contribution to the national economy as well as of the environment where this type of businesses operates. Analysis is based on secondary research and the majority of data used are mostly retrieved SME Policy Index, a benchmark tool designed to assess policies that support small and medium-sized enterprises (SMEs) in emerging economies and monitor progress in policy implementation over time. Over the last years, significant progress is made toward the establishment of the proper business environment for SMEs in Albania Government policies and initiatives seem to have produced concrete results with regard to some specific indicators institutional and regulatory framework for SME policy making; operational environment for SMEs; public procurement; standards and technical regulations; and internationalisation of SMEs Received: 30 September 2024 / Accepted: 28 November 2024 / Published: 3 December 2024

All-Sky Assimilation of GOES-16 Water Vapor Channels In Consideration of Cloud-Dependent Interchannel Observation-Error Correlations

Abstract The Advanced Baseline Imager (ABI) on the Geostationary Operational Environmental Satellite (GOES)-16 includes three water vapor channels (8, 9, 10) that are specifically designed to monitor tropospheric water vapor at high, middle, and low levels. This study investigates an effective approach for assimilating the all-sky brightness temperatures (BTs) derived from these three water vapor channels while considering the characteristics of interchannel observation-error correlations (IOECs). The NASA Unified Weather Research and Forecasting (NU-WRF) model, alongside the NCEP Gridpoint Statistical Interpolation (GSI)-based three-dimensional ensemble-variational hybrid data assimilation system, is utilized. First, an optimal bias correction (BC) scheme and data assimilation (DA) configuration, previously developed for all-sky assimilation of GOES-16 channel 8, are confirmed to be effective for channels 9 and 10. Then, the IOECs with respect to the symmetric cloud proxy variable among these three channels are analyzed. It is found that the IOECs display sigmoid function characteristics, being lower and roughly invariant with respect to under clear sky conditions and higher in cloudy conditions. Given the unique properties of IOECs, sensitivity experiments with various configurations for assimilating the all-sky BTs from the three water vapor channels are conducted with Hurricanes Laura (2020) and Ida (2021). The results indicate that a strategy combining the assimilation of all-sky BTs from one of the GOES-16 channels, especially channel 10, with clear-sky BTs from the other water vapor channels yields superior analysis and forecasts in most scenarios, thereby highlighting the importance of appropriately estimating and accounting for cloud-dependent IOECs when assimilating all-sky BTs from the infrared channels in operational DA systems.

Molecular Docking Study on Tamoxifen and Toremifene's Effects on the Breast Cancer Receptors

Both toremifene (TOR) and tamoxifen (TAM), selective estrogen receptor modulators, are equally effective therapies for breast cancer (BrCa). In high-risk women, anti-estrogenic tamoxifen is frequently used for both (BrCa) treatment and prevention. Another anti-estrogen that is successful in the treatment of (BrCa) is toremifene. Anti-estrogens have emerged as one of the most widely utilized medicine classes among women because (BrCa) is the most frequent malignancy in this population. Consequently, we performed a docking study to assess the effects of tamoxifen and toremifene therapy on the (BrCa) receptor. Tamoxifen and toremifene's interactions with the (BrCa) receptor were examined by a computational study of the ligand's binding. These receptors are named (1jnx), (1n5o), (1oqa), (1t2u), (1t29), (4igk), (4jlu), and (4y2g). All the docking has been done by software named Molecular Operating Environment (MOE) which was used to evaluate the binding docking and docking score between the ligand (TAM or TOR) with the (BrCa) receptors.

Acacia nilotica (L.) Delile as New Potential Inhibitors of 2019 Novel Coronavirus (Covid-19): Molecular Docking Study

The COVID-19 pandemic caused by SARS-CoV-2 has created an urgent need for effective therapeutics and vaccines. This study aimed to investigate the inhibitory activity of Acacia nilotica, a medicinal plant commonly used to treat various diseases in tropical and subtropical regions, against SARS-CoV-2 main proteases (Mpro) and spike proteins. Based on published literature, 22 compounds derived from Acacia nilotica were selected and assessed for their drug likeliness using Lipinski's rule of five and the SwissADME web tool. The compounds that fulfilled Lipinski's rule were subjected to molecular docking with Mpro (PDB ID: 6LU7) and spike proteins (PDB ID: 6LXT) using the Molecular Operating Environment software MOE. Among the 13 compounds docked with the main proteases and spike proteins of SARS-CoV-2, catechin-5-O-gallate, catechin-7-gallate, cetechin-3-O-gallate, cetechin-4-O-gallate, and gallocatechin-7-gallate was demonstrated superior inhibitory activity against Mpro and spike proteins compared to hydroxychloroquine, dexamethasone, and favipiravir. These findings indicate Acacia nilotica's potential as a source for developing specific therapeutic agents against SARS-CoV-2, pending further validation through wet lab experiments.

Hall effect thruster impedance characterization in ground-based vacuum test facilities

Hall effect thrusters (HETs) are typically regarded as DC electric propulsion devices as they are operated with isolated DC power supplies. However, it is well known that the HET’s discharge current possesses oscillations of varying magnitudes and frequencies and is thus a function of time with AC characteristics. The observed oscillations are caused by plasma processes associated with ion, electron, and neutral particle dynamics that occur inside the HET’s discharge channel and in the plume as the HET electrically interacts with its local operating environment. The extent to which plasma oscillations impact HET discharge dynamics is difficult to quantify due to the complexity of analyzing AC signals, given that the HET is a nonlinear, time-variant electrical load. In this work, we overcome the challenge of nonlinearity and time-variance of HETs by conducting a small-signal impedance analysis to characterize the effective resistance and reactance of the HET discharge with a novel and versatile impedance measurement diagnostic. The impedance magnitude and phase of a 7-kW class HET were measured from 100 Hz to 300 kHz with an excitation signal of ± 2 Vpk for two discharge operating conditions on krypton: 4.5 kW, 15 A and 6 kW, 20 A. The results were used to quantify resistive, capacitive, and inducive characteristics present within the HET discharge signature. For the 4.5 kW, 15 A thruster operating condition, the breathing mode capacitance was estimated to be 12.6 µF with an inductance of 15.3 µH. Furthermore, the impedance characteristics of the breathing mode are within ± 2.4 kHz of the power spectral density plots independently generated by time-resolved oscilloscope traces indicating good agreement in the frequency domain. Thus, the impedance measurement tool is a new diagnostic for characterizing the impedance and associated AC characteristics of HETs.

Safe deep reinforcement learning-assisted two-stage energy management for active power distribution networks with hydrogen fueling stations

In a power-hydrogen coupled integrated energy system (PHCIES), hydrogen fueling stations (HFSs) with solar photovoltaic (PV) systems are crucial devices to support hydrogen demand and maintain a stable and near-zero pollutant emission-based PHCIES operation by producing a pollutant-free hydrogen. However, optimal operation management of HFSs is challenging because the electrolyzers, compressor, hydrogen storage system (HSS), and fuel cell in HFSs are interconnected and change dynamically under various operational environments. To resolve this issue, this paper presents a safe deep reinforcement learning (DRL)-assisted two-stage framework that ensures a reliable and economical PHCIES operation. In the first stage, day-ahead operational schedules of stand-alone PV systems, PV-enabled HFSs, on-load tap changers, and capacitor banks with hourly resolution are coordinated to minimize the system operation cost, electricity arbitrage cost, PV curtailment cost, and real power loss by solving a Volt-VAR control (VVC) optimization problem. In the second stage, a safe DRL algorithm with a 15-min resolution is employed to minimize the real power loss and maximize the HFS profit by rescheduling the reactive power of stand-alone PV systems and real/reactive power and hydrogen of HFSs. The proposed self-tuning adaptive safety module integrated in the DRL method ensures no violations of HSS’ state-of-hydrogen (SOH) and voltage magnitude in the PHCIES during the training process. Numerical examples conducted on a PHCIES (IEEE 33-bus system coupled with two PV-enabled HFSs) demonstrate the effectiveness of the proposed framework in terms of training convergence, SOH/voltage violation, real power loss, and profit of HFSs.

Preparation and properties of a new type of temperature-resistant and salt-resistant fluid loss additive ADIM for oil well cement

In order to adapt to the deep high temperature and high salt cementing operation environment, itaconic acid (IA), N,N-dimethylacrylamide (DMAA), 2-acrylamido-2-methylpropanesulfonic acid (AMPS) and heterocyclic alkenes (R) were used as polymerization monomers in this paper. A temperature-resistant and salt-resistant fluid loss additive named ADIM was successfully prepared by free radical aqueous solution polymerization and orthogonal experiment. Thermogravimetric, infrared, nuclear magnetic resonance, and elemental analysis have been utilized for assessing the copolymers' structure and heat resistance. The results showed that the target copolymers with thermal decomposition temperature up to 300 ℃ were successfully prepared. A methodical evaluation was conducted on ADIM's application performance. The results show that when the ADIM content is 5 %, the water loss of fresh water cement slurry is only 44 mL at 220 ℃, and the water loss of saturated brine cement slurry is only 64 mL at 180 ℃.In addition to the excellent temperature resistance and salt resistance, the overall performance of ADIM is also good. The mechanism of ADIM was studied by mercury intrusion experiment, scanning electron microscope and TOC test. The results show that ADIM can be adsorbed on the cement particles, effectively reducing the porosity and pore size of the filter cake, thereby reducing the water loss of the cement slurry. ADIM has excellent temperature and salt resistance, which is of great significance for guiding the development of deep oil and gas resources.

Noise assessment of multirotor configurations during landing proceduresa).

This study numerically investigates the noise impact of multirotor aerial vehicles with different rotor scales during landing procedures. The operational environments of individual rotors are influenced by rotational speed and wake dynamics, leading to variations in landing noise characteristics. Noise impacts are evaluated across various landing operations from both physical and psychoacoustic perspectives using noise source hemispheres and noise maps. The physical noise impact is quantified using sound exposure level (SEL), while the psychoacoustic impact is assessed through a psychoacoustic annoyance (PA) based on sound quality metrics. Performance contours are established to compare noise impacts alongside other factors, such as energy consumption, landing duration, vehicle attitudes, and safety considerations. The combined effect of noise source strength and landing duration determines SEL, while PA is primarily influenced by acoustic loudness, which follows a similar trend to noise source strength. Consequently, physical and psychoacoustic noise impacts exhibit distinct trends based on the landing operations. This study outlines a process for optimizing landing operations that meet predefined performance goals while minimizing noise impacts. Because operational performance varies significantly across different landing procedures and vehicle types, the study emphasizes the importance of incorporating comprehensive performance criteria in the design of landing operations.

Hemisynthesis, Anti-Inflammatory, and In-silico Alpha-Amylase Inhibition of Novel Carlina Oxide Analogs

Background: Numerous natural products have been successfully developed for clinical use in the treatment of human diseases in almost every therapeutic area. Objective: This work aimed to synthesize some new analogs of Carlina oxide by functionalizing the fifth position of the furan by different acyl groups using the Friedel-Crafts acylation approach. The synthetic analogs and carlina oxide were then assessed for their in-vitro anti-inflammatory activity and in-silico alpha-amylase inhibition effect. Methods: The new analogs were synthesized at room temperature using different anhydrides with the presence of boron trifluoride diethyl etherate (BF3-Et2O) as an acid catalyst. A protein denaturation assay was performed to evaluate the anti-inflammatory activity, while the in-silico study was conducted using the Molecular Operating Environment (MOE) with different types of alphaamylase sources, such as human salivary pancreatic alpha-amylase and Aspergillus oryzae alphaamylase (PDB: 1Q4N, 5EMY, 7P4W respectively). Results: A total of four analogs of carlina oxide were obtained in yields of 60-7% and then identified with 1H and 13C NMR analysis. Additionally, analog 1 exhibited a better anti-inflammatory effect with an IC50 of 0.280 mg/mL. However, the in-silico study showed that all the synthetic analogs have different interactions with human salivary alpha-amylase (1Q4N) and other interactions with 5EMY and 7P4W. Conclusion: The new analogs of Carlina oxide have the potential to serve as an alternative agent for alpha-amylase inhibition, contributing to the reduction of postprandial hyperglycemia.

Introducing a simple convex hull method to calibrate diffusion coefficients in Lagrangian particle models

Calibrating Lagrangian particle models (LPMs) is crucial for simulating oil spill trajectories, enabling emergency responses, and mitigating harmful effects on the environment. However, there is a lack of rigorous observation data on oil spill events, especially in water bodies with no serious spills but perceived increasing risk, hampering LPM application. This study utilized trajectory data from three drifters in Lake Erie, collected in 2016, 2018, and 2019. We then proposed a simple and efficient convex hull method to identify the smallest envelope of the simulated particle cloud, covering all drifter trajectories while simultaneously controlling over-diffusion. This approach was employed to calibrate the General NOAA Operational Modeling Environment oil spill model (an LPM) and determine optimal diffusion coefficients. Based on 362 one-day-long drifter trajectory simulation cases, the recommended diffusion coefficient for particle trajectory simulation in Lake Erie was determined to be 14 m2 s−1. The calibrated model demonstrated the ability to effectively capture the movement direction of drifters and control the discrepancy between simulated and observed trajectories. Meanwhile, it underestimated drifter travel distances due to errors in input currents, the primary force driving drifter movement. These results promote LPM application for particle trajectory simulations.

Holistic Idle Periodic Evaluation method for mass balance monitoring at hydrogen refuelling stations

Hydrogen refuelling station (HRS) is an intricate system consisting typically of compression, storage, and dispensing subsystems. Thus, monitoring of HRSs has become a challenge in a real-time operation. Mass balance analysis of such a system consists of evaluating for a given timespan the amount of hydrogen received (produced or delivered on-site) and the amount of hydrogen dispensed to the vehicles. Previous work focused on off-line evaluation of hydrogen losses of HRSs with on-site hydrogen production. This article proposed a solution to holistically evaluate hydrogen mass balance and periodically evaluate hydrogen losses at HRSs in a continuous operation environment. We applied the Holistic Idle Periodic Evaluation method on real-time operational data of two HRSs with off-site hydrogen production and deployed the system for online monitoring of the hydrogen losses. The two stations were found to have hydrogen mass balance of 94.2% and 83.6% with an uncertainty of ±5.0%. Our method can track normal and anomalous system operations in real time. It is applicable for HRSs with both off-site and on-site hydrogen production and only requires one mass flow meter used for metering hydrogen being dispensed to vehicles.

Research on Video Monitoring Technology for Galloping of OCS Additional Conductors of High-Speed Railway in Strong Wind Zone

The strong wind environment causes the additional conductor of the overhead contact system (OCS) of the Lanzhou–Xinjiang high-speed railway to gallop, significantly impacting the safe operation of the train. This paper presents the design of an online monitoring system for the galloping of additional conductors in the OCS, utilizing video monitoring for accurate and real-time assessment. Initially, the dynamics of the OCS additional conductor and its operational environment are examined, leading to the selection of suitable data transmission and power supply methods to finalize the camera configuration. Secondly, a preprocessing method for enhancing images of galloping in OCS additional conductors is developed, effectively reducing noise in edge detection through a region chain code clustering analysis. The video monitoring system effectively extracts wire edges, addressing the issues of splitting, breakage, and edge overlap in edge detection, while accurately identifying wire targets in video images. In conclusion, a galloping monitoring test platform is established to extract galloping data from additional conductors through video monitoring. The analysis of the galloping frequency and amplitude facilitates the comprehensive monitoring and assessment of the galloping status of OCS additional conductors. The video monitoring system effectively extracts and analyzes galloping data of the OCS additional conductor, fulfilling the fundamental requirements for the online monitoring of additional conductor galloping, and possesses significant engineering application value.