Efficient Resource Research Articles

Co-substrate model development and validation on pure sugars and corncob hemicellulosic hydrolysate for xylitol production

A co-substrate model of Candida tropicalis TISTR 5306 cultivated in 10 - 100 g/L xylose and 1 - 10 g/L glucose at the ratio of 10:1 was developed based in part on modified Monod equation. The kinetic parameters include substrate limitation as well as substrate and product inhibitions with inclusion of threshold values. A general good fitting with average RSStotal, R2, and MStotal values of 162, 0.979, and 10.8, respectively, was achieved between ten simulated profiles and experimental kinetics data. The implementation of developed model on xylitol production from non-detoxified corncob hemicellulosic hydrolysate resulted in relatively good agreement with RSStotal, R2, and MStotal values of 368, 0.988, and 24.5, respectively. The developed model can be applied to predict microbial behavior in batch xylitol production system using hemicellulosic hydrolysate over a xylose range of 10 - 100 g/L and provide useful information for subsequent design of fed-batch and continuous systems to achieve the efficient sustainable resource management of this agricultural and agro-industrial waste.

A multi-index comprehensive evaluation method for assessing the water use balance between economic society and ecology considering efficiency-development-health-harmony.

Quantitative assessment of the water use balance between economic society and ecology (EEWB) is the basis for coordinating the competitive relationship of water use between human activity and ecological requirements and. It is of great significance for optimizing the water resources carrying capacity and achieving a healthy regional water balance. Based on the concept of harmonious balance, this paper puts forward the definition and connotation of EEWB regarding the competition in water use between economic society and ecology. And, a novel framework for assessing the EEWB is proposed. It has four aspects relating to water resources, economic society, ecology, and human-water relationship. Linked to these aspects the Data Envelopment Analysis (DEA) technique, Water Ecological Footprint (WEF) model, InVEST model and indicators system of human-water relationship are used to establish a water resources efficiency index (IEEWB-W), economic society high-quality development index (IEEWB-ES), ecology health index (IEEWB-E), harmony index of human-water relationship (IEEWB-H). The four indices were then integrated into the water use balance between economic society and ecology index (IEEWB) with Euclidean distance, thus forming the EEWB quantification method system. Finally, the temporal and spatial characteristics of EEWB during 2010-2022 was diagnosed in Henan Province and cities of China. Results reveal that: (1) The water resources utilization efficiency exhibit a changing trend of initial decrease followed by subsequent increase; (2) Southern cities in Henan Province have a higher economic society development level compared to northern cities; (3) IEEWB-E in Henan Province is below 0.60, indicating that the regional ecology health remains consistently vulnerable; (4) IEEWB-H in Henan Province shows an increasing trend, indicating that a gradual improvement and overall upward development in the human-water relationship; (5) IEEWB multi-year average was within [0.53, 0.65] in Henan Province, indicating a state of Proximity imbalance. The low level of ecological health is the primary influencing factor. These findings will contribute to a better understanding of the water use balance between economic society and ecology and provide scientific reference for achieving a healthy regional water balance.

Water-use Efficiency with Machine Learning: A Global Perspective

Water scarcity is a pressing social, economic, and environmental issue with significant corporate sustainability and planetary health implications. Therefore, improving the efficiency of water resources use is an important research issue. With the rapid development of machine learning technology, relying on the learning ability of learning algorithms and the support of computer computing power, it is possible to optimize the global allocation of water resources through machine learning technology. This paper investigates the progress made by machine learning methods in water use efficiency. Specifically, this paper introduces the basic principle of the machine learning method for evaluating water resources efficiency, including classification and regression tasks. Then, several mainstream evaluation indexes of water resource utilization efficiency are introduced. Finally, this paper analyzes the main challenges and future development direction of machine learning to improve the efficiency of water resources utilization. This paper aims to provide a complete research view for researchers using machine learning methods to improve water efficiency.

The Effect of Financial Leverage, Profitability and Dividend Policy on Firm Value

This research provides valuable insights into the factors influencing company value within the manufacturing sector, specifically the cement subsector in Indonesia. Financial leverage, profitability, and dividend policy are key variables examined, as they significantly impact a company's financial health and market perception. Financial leverage reflects a company’s reliance on debt financing relative to equity, and the study shows that higher leverage can positively affect company value if managed effectively, thereby increasing returns for shareholders. Profitability, often measured through indicators such as Return on Assets (ROA) or Return on Equity (ROE), also plays a significant role in enhancing company value, as it reflects efficient resource management and attracts investor interest. Additionally, a consistent dividend policy reinforces the company’s reputation and draws investors, contributing positively to company value in the stock market. The findings emphasize that cement subsector companies with balanced leverage, solid profitability, and a consistent dividend policy tend to perform better in terms of company value, providing essential insights for stakeholders in optimizing strategies to maximize company value.

Improving QoS in cloud resources scheduling using dynamic clustering algorithm and SM‐CDC scheduling model

SummaryQuality of Service (QoS) regulates and controls network resources by setting priorities for specific data types. Many clustering algorithms are used to cluster cloud workloads, most of which are static. However, the lack of dynamic algorithms is seen in the face of huge databases that are real‐time and according to the existing clustering conditions. Additionally, fair allocation of tasks on servers and efficient resource utilization pose challenges. In this research, two solutions are proposed to improve the quality of service: the first solution uses the chameleon dynamic algorithm, a method to improve service quality. The chameleon algorithm has been able to show significant performance due to its high accuracy in detecting the smallest distance between clusters. This dynamic algorithm outperforms static algorithms with classification accuracy and response speed, which are the most important parameters of service quality. The second part of the proposed solution is to use the Scheduling Model using Cloud Data Centers (SM‐CDC) system to select the best service provider based on the clustering done in the previous step. A SM‐CDC technique is developed to handle cloud storage center tasks that are stored in electronic devices. According to the comparison with existing scheduling policies, SM‐CDC offered 36% decrease on response time, 50% reduction on cost of resources, and 40% improvement on QoS Satisfaction.

Study on the Vertical Propagation Behavior of Hydraulic Fractures in Thin Interbedded Tight Sandstone

Hydraulic fracturing technology is vital for the efficient extraction of oil and gas from low-permeability tight sandstone reservoirs.Taking the central Bohai oilfield in China as an example, these fields are typically composed of thinly interbedded tight sandstone, characterized by low permeability and significant lithological heterogeneity between layers. Fractures may either be confined, limiting vertical growth and reducing production, or overextend into water-bearing zones, causing contamination and compromising reservoir integrity. Therefore, predicting vertical fracture propagation during field fracturing operations is critical for efficient resource extraction.However, there is still a lack of comprehensive understanding of the mechanisms governing vertical fracture growth offshore.This paper applies numerical simulations based on the finite element method to elucidate the interlayer fracture propagation behavior in low-permeability tight sandstone reservoirs. A fracture propagation model for thin interlayered tight sandstone formations is constructed, and the effects of various factors on hydraulic fracture propagation are systematically analyzed, including geological factors such as interlayer stress contrast, thickness, and differences in elastic modulus, as well as operational parameters including fracturing fluid viscosity and injection rate. This study clarifies the cross-layer propagation patterns of hydraulic fractures under the influence of multiple factors and yields a comprehensive prediction chart for fracture propagation thickness under the combination of complex factors. The results of this research can provide theoretical support for the design of reservoir stimulation operations in low-permeability tight sandstone oilfields.

Artificial Intelligence and Environmental Sustainability: Insights from PLS-SEM on Resource Efficiency and Carbon Emission Reduction

This study investigates the relationship between Artificial Intelligence (AI) and environmental sustainability, focusing on how AI-driven resource efficiency, energy consumption, environmental monitoring, and carbon emission reduction contribute to sustainability outcomes. The purpose of this research is to examine the dual nature of AI's impact on sustainability by testing both positive and negative effects using Partial Least Squares Structural Equation Modeling (PLS-SEM). Drawing from a sample of 233 firms in the energy, transportation, and manufacturing sectors, this study collects data through an online survey measured on a five-point Likert scale. Four hypotheses are tested, revealing that AI-driven resource efficiency and environmental monitoring positively affect sustainability, while AI energy consumption has a negative impact. Furthermore, AI integration in industrial processes helps reduce resource depletion. The findings suggest that governments should incentivize AI adoption aimed at resource efficiency and environmental monitoring through policies like tax breaks or subsidies, particularly for firms reducing their carbon footprint. To mitigate the negative effects of AI energy consumption, policymakers are urged to promote energy-efficient AI models and invest in renewable energy infrastructure. A balanced policy approach is crucial to optimize the environmental benefits of AI while minimizing its energy-related drawbacks.

تأثير التحول الرقمي على كفاءة الموارد البشرية في المملكة العربية السعودية

Digital transformation is one of the most significant modern mechanism concerned with restructuring the traditional labor market into digital work as a result of keeping pace with technical and technological progress. In the Kingdom of Saudi Arabia, Vision 2030 emphasized the activation and development of digital transformation in all government and private sectors, therefore, the aim of this study is to identify the impact of digital transformation on the efficiency of human resources, and determine the potential benefits of adopting digital transformation on the development of human resources. Furthermore, the study attempts to find whether there is link between the impact of demographic characteristics variables, such as (age, academic qualification, years of experience) on efficiency, identifying the concept and benefits of digital transformation. The questionnaire as a tool to suit the nature of the study, and the study population consisted of workers in Saudi banks, and it was distributed in a simple random way, and the number of responses reached (45) male and female employees, and the SPSS statistical analysis program was used to analyze the data. As one of the most important findings of the two researchers is the existence of a statistically significant positive correlation relationship between digital transformation and human resource efficiency, as the Pearson correlation coefficient reached (0.726), which is a statistically significant value at the level of significance (0.01), and there are no statistically significant differences in organizational efficiency. Referring to any of the demographic variables, the analysis showed that the level of organizational efficiency in the sample community is high by a percentage of 91.1%, in light of the findings of the study. The two researchers recommended expanding training centers on technological programs and activating them to develop human resources, and educate organizations and society about the advantages of Modern technology and its positive impact on the efficiency of employees, raising the spirit of competition and cooperation between organizations to create new digital growth that seeks to achieve the vision of 2030.

Prediction of catchment efficiency in direct energy deposition using dimensional analysis and machine learning

Direct Energy Deposition (DED) is an additive manufacturing process used to fabricate thin/thick-walled structures and high-value component restorations. As blown powder-based DED gains prominence, catchment efficiency becomes imperative for enhancing material utilization and process effectiveness. This study focuses on predicting catchment efficiency within a DED process by investigating four significant process parameters: laser power, powder feed rate, carrier gas flow rate, and deposition speed. The primary objective is to devise a dimensionless number capable of predicting catchment efficiency based on a combination of these parameters, categorizing efficiency into low, medium, and high regions. This approach reduces the need for resource-intensive experimentation. The experimental validation of the proposed dimensionless number was conducted, showing an error rate of around 8%. Additionally, six machine learning classifier algorithms – Support Vector Machines, K-Nearest Neighbours, Decision Tree, Random Forest, Linear Regression, and Gaussian Naive Bayes – were employed to categorize catchment efficiency zones. Support Vector Machine demonstrated the best performance with a predictive accuracy of 0.98. This study provides a methodology for catchment efficiency prediction, enhancing decision-making and resource optimization, and can be used to optimize process parameters for thin-wall or relatively thin-wall fabrication.

Automatic Scheduling Method for Customs Inspection Vehicle Relocation Based on Automotive Electronic Identification and Biometric Recognition

This study presents an innovative automatic scheduling method for the relocation of customs inspection vehicles, leveraging Vehicle Electronic Identification (EVI) and biometric recognition technologies. With the expansion of global trade, customs authorities face increasing pressure to enhance logistics efficiency. Traditional vehicle scheduling often relies on manual processes and simplistic algorithms, resulting in prolonged waiting times and inefficient resource allocation. This research addresses these challenges by integrating EVI and biometric systems into a comprehensive framework aimed at improving vehicle scheduling. The proposed method utilizes genetic algorithms and intelligent optimization techniques to dynamically allocate resources and prioritize vehicle movements based on real-time data. EVI technology facilitates rapid identification of vehicles entering customs facilities, while biometric recognition ensures that only authorized personnel can operate specific vehicles. This dual-layered approach enhances security and streamlines the inspection process, significantly reducing delays. A thorough analysis of the existing literature on customs vehicle scheduling identifies key limitations in current methodologies. The automatic scheduling algorithm is detailed, encompassing vehicle prioritization criteria, dynamic path planning, and real-time driver assignment. The genetic algorithm framework allows for adaptive responses to varying operational conditions. Extensive simulations using real-world data from customs operations validate the effectiveness of the proposed method. Results indicate a significant reduction in vehicle waiting times—up to 30%—and an increase in resource utilization rates by approximately 25%. These findings demonstrate the potential of integrating EVI and biometric technologies to transform customs logistics management. Additionally, a comparison against state-of-the-art scheduling algorithms, such as NSGA-II and MOEA/D, reveals superior efficiency and adaptability. This research not only addresses pressing challenges faced by customs authorities but also contributes to optimizing logistics operations more broadly. In conclusion, the automatic scheduling method presented represents a significant advancement in customs logistics, providing a robust solution for managing complex vehicle scheduling scenarios. Future research directions will focus on refining the algorithm to handle peak traffic periods and exploring predictive analytics for enhanced scheduling optimization. Advancements in the intersection of technology and logistics aim to support more efficient and secure customs operations globally.

Georgia’s potentials for sustainable intensification, increasing food security and rural incomes.

Abstract Increasing global demand for agricultural commodities spurs conversions of natural ecosystems. Sustainable intensification in areas of high yield gaps has been proposed a pathway to achieve food security, support rural livelihoods, while also reducing the impact of commodity production by narrowing yield gaps on existing agricultural lands, while improving resource efficiency. Following the dissolution of the Union of Soviet Socialist Republics (USSR), Georgia experienced one of the highest losses of agricultural productivity among all former USSR countries and is now highly dependent on food imports. Closing yield gaps in Georgia through sustainable intensification has the potential to increase food self-sufficiency, support rural livelihoods, and strengthen food security and sovereignty. We estimated its potential for sustainable intensification in current agricultural areas to achieve self-sufficiency for wheat, maize, and barley. We found that crop yields can be doubled to tripled under high-input production systems, using high-yielding varieties, optimized inputs, fertilizers, and pest control. Yet, self-sufficiency in wheat can only be reached if at least 80% of the potentially attainable yields are achieved and if land is strategically allocated between crops. To achieve such increases, farmers need access to and training for using different crop varieties, fertilizers, and pest and disease control practices and products. Intensification increases the risks to ecosystem services’ health and livelihoods, particularly raising equity concerns. Yet, intensifying very low input systems is often found to be more sustainable, with high yield increases compared to limited impacts on the environment. The high employment rate in the agricultural sector in Georgia particularly provides opportunities to reduce poverty and increase livelihoods through increasing incomes and food security.

Lifelong QoT prediction: an adaptation to real-world optical networks

Predicting the quality of transmission (QoT) is a critical task in the management and optimization of modern fiber-optic networks. Traditional machine learning (ML) QoT prediction models, typically trained on pre-collected datasets, are designed to make long-term predictions once deployed. However, this static training strategy often falls short in the face of time-dependent network evolution and variations. We identify the root cause of these shortcomings as shifts in data distribution, which are not accounted for in conventional static models. In response to these challenges, we propose an online continual learning pipeline that is specifically designed for stable QoT prediction in optical networks. This pipeline directly addresses the problem of distribution shifts by continuously updating the prediction model in response to real-time network data. We explore and compare various strategies within this framework and demonstrate that the integration of the adaptive retraining strategy and the regularized online continual learning algorithm (OCL-REG) significantly enhances the QoT prediction stability while optimizing the resource efficiency. OCL-REG demonstrates superior adaptability and stability, achieving an average cumulative mean squared error (C-MSE) of 0.19 on a testbench with a data distribution shift sequence containing 1000 batches. Moreover, the OCL-REG model requires fewer samples for adaptation, averaging around 107 samples, compared to the conventional retraining strategy, which requires an average of 253 samples. Our approach presents a paradigm shift in QoT prediction, moving from a static to a dynamic, lifelong learning model that is more attuned to the evolving realities of real fiber-optic networks.

Rethinking Local Government Asset Management in Indonesia: Governance, Planning, and Policy Evaluation

Effective asset management is essential for enhancing operational efficiency and achieving sustainable development within local governments in Indonesia. This study explores key challenges in asset management, focusing on governance, planning, and policy evaluation. Several critical issues are identified: fragmented governance, insufficient transparency and coordination, outdated asset inventories, and misalignment between asset management practices and broader development goals. Policy evaluation mechanisms are also inadequate, failing to measure asset utilisation effectively. These challenges lead to inefficiencies in resource allocation, underutilisation of assets, and missed opportunities for local economic growth. The findings align with Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities), which promotes efficient resource use, and SDG 9 (Industry, Innovation, and Infrastructure), which supports enhanced infrastructure management. Addressing these goals enables local governments to align asset management strategies with broader sustainability objectives, fostering equitable growth and long-term development. Through a qualitative literature review, this study analyses data from government reports and prior research to highlight these challenges and propose solutions. An integrated approach—aligning governance frameworks, improving asset management planning, and strengthening policy evaluation—can overcome the identified barriers. By adopting this model, local governments can optimise asset management, leading to improved efficiency, better resource utilisation, and sustainable development. Future research should explore applying this framework across regions in Indonesia to validate its effectiveness.

DFRDRL: a dynamic fuzzy routing algorithm based on deep reinforcement learning with guaranteed latency and bandwidth for software-defined networks

Traditional routing algorithms have several limitations that become increasingly significant in the context of Software-Defined Networking (SDN). Firstly, these algorithms often have limited support for Quality of Service (QoS) requirements, making them less suitable for handling diverse traffic types efficiently. Moreover, current SDN controllers leverage a default shortest-path routing approach, which does not allow for more dynamic and flexible traffic management based on real-time network policies. To address these issues, this paper introduces a Dynamic Fuzzy Routing algorithm based on Deep Reinforcement Learning (DRL) for SDN that provides guaranteed latency and bandwidth (DFRDRL). DFRDRL provides intelligent and efficient routing that adapts to dynamic traffic by considering path-state criteria and using DRL. Fuzzy logic mainly performs online routing between an ingress-egress pair. To adapt to dynamic traffic changes, DFRDRL can predict the traffic matrix in real time. Meanwhile, DFRDRL reduces the routing reliance on network topology by weighting the network based on critical nodes. Additionally, when the network experiences congestion based on the traffic matrix, a deferral mechanism is applied to prioritize requests with lower resource demands. This mechanism helps ensure efficient resource allocation by temporarily postponing or queuing higher-demand requests, thereby optimizing overall network performance during periods of congestion. The simulation results show that the performance of DFRDRL is better than the equivalent algorithms in terms of latency and throughput. Also, DFRDRL is about 2.5% more efficient than the best existing algorithm in terms of admission rate of routing requests.

Efficient resource allocation scheme for dually connected non-orthogonal multiple access based LIFI-RF networks

Efficient resource allocation scheme for dually connected non-orthogonal multiple access based LIFI-RF networks

Effect of exogenous ketone supplementation on cardiac energetics, function, and perfusion in type 2 diabetes, heart failure, and healthy participants- A single center, open labelled clinical study

Abstract Background Type 2 diabetes (T2D) confers loss of cardiac metabolic flexibility in fuel selection and impaired mitochondrial function. Enhanced ketone metabolism observed in T2D may represent a compensatory adaptive mechanism as ketones are a more energy efficient resource than carbohydrates or fatty acids. Favorable energetic properties of ketones may potentially mitigate the energy starved state in T2D and heart failure (HF). Purpose To test whether exogenous oral ketone supplementation would reverse cardiac energetic deficit, as well as improve cardiac function and perfusion in patients with T2D, with HF with reduced ejection fraction (HFrEF), and healthy volunteers (HV). Methods Three groups of patients were recruited: patients with T2D (n=33); patients with HFrEF (n=29, 14 with T2D); and HVs(n=25). Participants underwent cardiovascular magnetic resonance and spectroscopy to assess left ventricular (LV) function, rest and dobutamine stress perfusion and energetics (phosphocreatine/adenosine triphosphate ratio[PCr/ATP]). After the initial visit, participants had an identical second visit following 2 weeks of oral ketone supplementation (30g daily). Results Participants were matched in age and sex distribution. There were no significant differences in LV ejection fraction (LVEF) between T2D and HV groups with significantly lower LVEF in HFrEF group. Both the T2D group and the HFrEF group showed significant reductions in PCr/ATP ratio and the rest and stress GLS compared to the HV. The resting and dobutamine-stress myocardial blood flow (MBF) were only significantly reduced in patients with HFrEF compared to T2D and HV groups. Subgroup analyses of HFrEF group with and without T2D showed no significant differences in energetics, perfusion, or functional assessments. Significant increase in PCr/ATP ratio was seen only in T2D group from baseline after 2 weeks of ketone supplementation (Visit-1:1.6[1.5,1.8] vs Visit-2:1.9[1.7,2.1]; P=0.01). This significant increment was not observed in HFrEF or HV groups. Two weeks of ketone supplementation was not associated with any significant changes in LVEF, GLS, rest or dobutamine-stress myocardial blood flow, or in myocardial lipid content (Table-1). Conclusions Short-term ketone supplementation is associated with isolated significant improvements in resting cardiac energetics in patients with T2D and preserved LVEF, without changes cardiac function or perfusion. In contrast to patients with T2D and preserved LVEF, short-term ketone supplementation does not lead to any significant improvements in energetics in patients with HFrEF suggesting loss of mitochondrial plasticity with transition to HFrEF. Moreover, healthy volunteers experience no further increments of myocardial energetics from the normal range values at baseline with ketone supplementation.

Temporal trends, regional and socio-economic disparities of colorectal cancer burden in Cyprus

Abstract Background Colorectal cancer (CRC) is one of the main causes of mortality and morbidity worldwide and it is the third most common cancer in Cyprus. Previous studies have documented variations in CRC burden over time, across regions and area-based socio-economic deprivation. The aim of this study was to analyse the burden of CRC over time, across regions and area-based socio-economic deprivation in Cyprus for the time period 2000 to 2015. Methods A small area ecological study was performed, with census tracts as units of spatial analysis. The incidence and death date, sex and age of CRC were obtained from the population-based cancer registry of Cyprus. Indirect standardization was used to calculate the sex and age Standardize Incidence Ratios (SIRs) and Standardized Mortality Ratios (SMRs) of CRC while the smoothed estimates of SIRs and SMRs were derived from the Spatiotemporal Bayesian Poisson log-linear model. To investigate temporal trends in the association between area-based socioeconomic deprivation and CRC burden, we have used the national socio-economic index. Results The burden of CRC was rising over time. The CRC cumulative incidence increase from 243.7 in the time period 2000-2007 to 347.3 per 100,000 persons in the time period 2008-2015.There was also a more pronounced geographical variation in SIR and SMR in the time period 2008-2015. Four areas out of 369 had smoothed SIR>1.15 while twenty-six areas had smoothed SMR>1.15, with most of those areas located at the east coast of the island and around the capital city. There was a temporal trend in the socio-economic inequalities of CRC burden. The areas in the third and fourth quartile of the socio-economic index had 23% and 45% lower rates of CRC incidence, and 33% and 47% lower rates of CRC mortality in the period 2008-2015. Conclusions These findings could shape national prevention policies, such as the national screening program of CRC starting in 2025 and protection strategies for CRC mortality. Key messages • Colorectal cancer screening should be geographically targeted for efficient resource allocation. • Public health interventions to improve colorectal-specific mortality risk should be geographically targeted.

Optimizing data transmission in 6G software defined networks using deep reinforcement learning for next generation of virtual environments

Data transmission of Virtual Reality (VR) plays an important role in delivering a powerful VR experience. This increasing demand on both high bandwidth and low latency. 6G emerging technologies like Software Defined Network (SDN) and resource slicing are acting as promising technologies for addressing the transmission requirements of VR users. Efficient resource management becomes dominant to ensure a satisfactory user experience. The integration of Deep Reinforcement Learning (DRL) allows for dynamic network resource balancing, minimizing communication latency and maximizing data transmission rates wirelessly. Employing slicing techniques further aids in managing distributed resources across the network for different services as enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low Latency Communications (URLLC). The proposed VR-based SDN system model for 6G cellular networks facilitates centralized administration of resources, enhancing communication between VR users. This innovative solution seeks to contribute to the effective and streamlined resource management essential for VR video transmission in 6G cellular networks. The utilization of Deep Reinforcement Learning (DRL) approaches, is presented as an alternative solution, showcasing significant performance and feature distinctions through comparative results. Our results show that implementing strategies based on DRL leads to a considerable improvement in the resource management process as well as in the achievable data rate and a reduction in the necessary latency in dynamic and large scale networks.

An efficient Protocol for Enhanced Flynet Communication in Presence of Partitioning

This paper presents an adaptive fault-tolerant resource allocation protocol tailored for FlyNet, a dynamic aerial network characterized by its mobility and three-dimensional operational space. Addressing the challenges of network partitioning and resource allocation in aerial networks, the proposed algorithm dynamically adjusts to the network's changing topology, ensuring consistent and efficient resource management. Through robust fault tolerance mechanisms, the protocol enhances FlyNet's reliability, maintaining seamless communication and optimal resource utilization even amid node mobility and disruptions. Simulation results demonstrate the algorithm's effectiveness in adapting to dynamic environments, maximizing resource utilization, and minimizing communication delays.

Integrated geophysical and geospatial techniques for surface and groundwater modeling

An integrated approach using geophysical and geospatial techniques was employed to model the surface and subsurface water-bearing strata and assess aquifer vulnerability in the Sehnsa town, Kotli district, State of Azad Kashmir, Pakistan. The inadequate scientific studies in the hilly terrain with such complex geological conditions has led to the failure of the boreholes for groundwater extraction. For the evaluation of groundwater potential and subsurface lithology, 30 vertical electrical soundings (VES) stations utilizing the Schlumberger electrode configuration were completed, modeled and analyzed spatially. Numerous geoelectrical parameters like true resistivity, thickness of subsurface layers and Dar-Zarrouk parameters were evaluated. The subsurface lithology delineated comprised topsoil, clayey sand, sandstone, and boulder clays which closely resemble to the borehole lithologs available in the study area. The inversion model confirms the presence of patches of high-resistivity sandstone in the southwestern part of the study area with the maximum thickness of the aquifer up to 140 m. Most aquifers were classified as unconfined with Q–type resistivity curves. The protective overburden capacity of the aquifers is rated as poor at VES 1, 3–5, 8, 10–16, 18, 19, 22–25, 27 and 30 whereas the moderate category was found at VES 2, 9 and 20 and excellent at VES 7 and 28, respectively. Therefore, the VES stations with poor and moderate ratings of overburden protective capacity are vulnerable for surface contaminants. The aquifer recharge was associated with rainfall and partly from the Poonch River. The effective integration of geophysical and geospatial techniques in this study provides sufficient information about the regional water resources and gives a preliminary model that can facilitate efficient water resource management in the area. These approaches can be successfully applied to diverse geographical and hydrogeological sites due to their versatility and reliability.