Energy Transport Research Articles

QoS Technology in Computer Communication Transmission Network Security under the Artificial Intelligence

QoS Technology in Computer Communication Transmission Network Security under the Artificial Intelligence

A one-step fabrication of soft-magnetic high entropy alloy fiber with excellent strength and flexibility

Soft-magnetic fibers (SMFs) play a crucial role in energy conversion, transmission, and storage within electronic devices. However, conventional SMFs have poor plasticity and are therefore difficult to withstand long-term tensile, torsional, and shear deformation. A high fraction of grain boundaries could improve plastic deformability of conventional SMFs, but deteriorates the coercivity. This severely limits their applications in flexible electronics and multifunctional components. Herein, we propose a strategy to overcome this dilemma, which is realized by coarsening the grains of a Fe34Co29Ni29Al3Ta3Si2 high entropy alloy (HEA) fiber containing ordered coherent nanoprecipitates with small lattice misfit via a simple one-step in-rotating-water spinning method. This allows to reduce domain wall pinning and improve dislocation mobility. The resultant micron-diameter soft-magnetic HEA fiber has a tensile strength of 674 MPa at 23% elongation, a low coercivity of 8.1 Oe, a moderate magnetization of 116 emu/g at 10 kOe and a high Curie temperature of 770 K.

A Novel Optimization Algorithm Inspired by Egyptian Stray Dogs for Solving Multi-Objective Optimal Power Flow Problems

One of the most important issues that can significantly affect the electric power network’s ability to operate sustainably is the optimal power flow (OPF) problem. It involves reaching the most efficient operating conditions for the electrical networks while maintaining reliability and systems constraints. Solving the OPF problem in transmission networks lowers three critical expenses: operation costs, transmission losses, and voltage drops. The OPF is characterized by the nonlinearity and nonconvexity behavior due to the power flow equations, which define the relationship between power generation, load demand, and network component physical constraints. The solution space for OPF is massive and multimodal, making optimization a challenging concern that calls for advanced mathematics and computational methods. This paper introduces an innovative metaheuristic algorithm, the Egyptian Stray Dog Optimization (ESDO), inspired by the behavior of Egyptian stray dogs and used for solving both single and multi-objective optimal power flow problems concerning the transmission networks. The proposed technique is compared with the particle swarm optimization (PSO), multi-verse optimization (MVO), grasshopper optimization (GOA), and Harris hawk optimization (HHO) and hippopotamus optimization (HO) algorithms through MATLAB simulations by applying them to the IEEE 30-bus system under various operational circumstances. The results obtained indicate that, in comparison to other used algorithms, the suggested technique gives a significantly enhanced performance in solving the OPF problem.

Enhanced fault distance estimation for robust protection in unbalanced active distribution networks

Active Distribution Networks (ADN) have evolved significantly by integrating renewable energy sources. While these advancements have ushered in a more sustainable and efficient power system, these have also introduced challenges for traditional protection strategies. Conventional methods, often reliant on overcurrent relays, can struggle to adapt to the complexities of active distribution networks, where fault scenarios are diverse and dynamic. This paper identifies the limitations of conventional protection strategies and explores the redundancy concept applied to protection systems. To improve protection system reliability, an approach that overcomes these limitations using the advantages of distance protection is proposed here. The distance-based protection relay, traditionally employed in transmission networks, offers a promising redundancy solution for the challenges imposed by ADN. Considering the previous exposure, this paper introduces an algorithm for fault resistance and distance estimation designed to optimise the operation of distance relays in active distribution networks. The approach achieves remarkable accuracy with consistently low estimation errors for fault resistance and distance, regardless of fault type or distance. The proposed distance-based protection method is poised to revolutionise fault analysis in ADN, ultimately leading to faster fault isolation and more reliable and improved grid resilience.

Problem-tailored Simulation of Energy Transport on Noisy Quantum Computers

The transport of conserved quantities like spin and charge is fundamental to characterizing the behavior of quantum many-body systems. Numerically simulating such dynamics is generically challenging, which motivates the consideration of quantum computing strategies. However, the relatively high gate errors and limited coherence times of today's quantum computers pose their own challenge, highlighting the need to be frugal with quantum resources. In this work we report simulations on quantum hardware of infinite-temperature energy transport in the mixed-field Ising chain, a paradigmatic many-body system that can exhibit a range of transport behaviors at intermediate times. We consider a chain with L=12 sites and find results broadly consistent with those from ideal circuit simulators over 90 Trotter steps, containing up to 990 entangling gates. To obtain these results, we use two key problem-tailored insights. First, we identify a convenient basis–the Pauli Y basis–in which to sample the infinite-temperature trace and provide theoretical and numerical justifications for its efficiency relative to, e.g., the computational basis. Second, in addition to a variety of problem-agnostic error mitigation strategies, we employ a renormalization strategy that compensates for global nonconservation of energy due to device noise. We discuss the applicability of the proposed sampling approach beyond the mixed-field Ising chain and formulate a variational method to search for a sampling basis with small sample-to-sample fluctuations for an arbitrary Hamiltonian. This opens the door to applying these techniques in more general models.

Credit constraints and willingness to pay for electricity among non-connected households in Zambia

Access to electricity in Zambia remains low, with most households dependent on traditional energy sources. This study analyzes data from a contingent valuation (CV) survey conducted by the World Bank to estimate household willingness to pay (WTP) for electricity connections among non-connected households. It evaluates two electrification technologies: grid electricity and solar home systems (SHS), categorized into low-capacity and high-capacity systems. The findings show that WTP is positive across technologies and increases with extended payment options. Mean WTP for grid electricity and high-capacity SHS exceeds market prices, indicating potential for private sector involvement in expanding access. In contrast, low-capacity SHS has a mean WTP below market prices, suggesting that subsidies are needed for viable private sector participation. This highlights the importance of offering electricity technologies that provide more than just lighting, particularly in rural areas where high-capacity systems can foster economic activities. The study also emphasizes the necessity for households to access credit financing or subsidies to afford market prices for solar PV systems and to manage their consumption effectively. Additionally, social factors such as housing quality and educational attainment significantly influence mean WTP for electricity connections across the various technologies.

Comparative analysis of two static var compensator models in voltage control of transmission network

The static var compensator (SVC) is a member of the family of flexible alternating current transmission systems controllers used in power system engineering to manage specific transmission network characteristics to enhance the performance of the transmission networks and thus increase the networks’ reliability. Power system engineers typically find it difficult to choose which SVC model to implement for simulations. This research aims to address this issue by conducting a comparative examination of two key SVC models on a transmission network. The two models of SVC variable shunt susceptance and firing angle were mathematically modeled and methodically included into the Newton-Raphson power flow algorithm for the network power flow solution. The IEEE 30-bus network was adopted as the test case, and the method was implemented in the MATLAB/Simulink environment. The network performance metric utilized was the voltage profile of the network. The two SVC models were successful in enhancing the network's performance; however, the variable shunt susceptance model was computationally faster than the firing angle model, as revealed by the simulation results. Therefore, among the two SVC models, the variable shunt susceptance model may be taken into account for simulation to enhance the performance of the transmission networks.

Aerosol-Induced Changes in Atmospheric and Oceanic Heat Transports in the CESM2 Large Ensemble

Abstract The total poleward energy transport (PET) is set by the top of atmosphere radiation flux and is therefore sensitive to any process which can alter those fluxes, particularly in the shortwave. One example is the direct and indirect effects of anthropogenic aerosols, which increase the local reflection of solar radiation back into space. The historic emission of sulfur dioxide, which peaked in the northern midlatitudes during the 1980s, has been proposed as a primary contributor to historic anomalies in cross-equatorial energy transport, as well as related processes such as a shift in the tropical rainband. In this study, we analyze simulations from the Community Earth System Model, version 2 (CESM2), large ensemble and single-forcing projects to better understand the forced response of PET to historical forcings. First, analysis of the single-forcing project reveals that the position of the intertropical convergence zone (ITCZ) responds in a nonlinear manner to greenhouse gas forcing in CESM2. This type of nonlinearity has been found previously in the context of the aerosol-only simulations in the CESM2 single-forcing project but may be the first identification of a similar effect in the greenhouse gas–only simulations. Second, through analysis of the full CESM2 large ensemble simulations, we find that anomalous heat transport occurred in both the atmosphere (through the mean meridional circulation and atmospheric eddies) and the oceans (through the Atlantic and Indo-Pacific sectors) due to a variety of related processes including the Hadley cells, the midlatitude storm tracks, the Atlantic meridional overturning circulation (AMOC), and the Pacific wind-driven subtropical gyre. Significance Statement In this study, we investigate how the Earth system changed the transport of energy from the tropics to the poles in response to historic pollution. We analyze a large number of climate model simulations of the recent past (1850 to present) and find that historic emissions of sulfur dioxide caused the model to transport more energy in the form of stronger ocean currents, stronger storms, and stronger prevailing winds. This is because the modeled currents in the Atlantic were too sensitive to historic pollution and transported too much warm water northward.

Self-organized efficient transmission in dynamic networks

Effective protocols are needed to ensure successful transmission of signals, messages, or parcels through a network of intermediaries. Existing studies often overlook real-world constraints such as limited resources. We investigate the design of efficient and robust transmission strategies in dynamic networks subject to dissipation and resource limited node activity, as in wireless sensor networks. We find that dissipation and randomness can enhance transmission efficiency and network resilience. For a broad family of protocols, a self-organized dynamics assures global connectivity, even in the limit of low density of links, where random networks fail.

Multifunctional III-nitride optoelectronic system on a tiny chip

Multi-quantum well (MQW) diodes exhibit simultaneous emission and detection, allowing them to serve as multifunctional devices, including light emitters, receivers, energy transmitters, and information transmitters. Leveraging this capability, we designed a Multifunctional Energy Transfer Information System (METIS) that integrates contactless control, energy harvesting, and information transfer. At the core of this system, the multifunctional energy communication chip operates effectively across a broad range of extreme temperatures and in various solution environments. As the ambient temperature varies from −60 to 120 °C, the peak emission wavelength shifts from 465 to 476 nm, and even with further temperature changes from −70 to 150 °C, the communication function remains stable. Encapsulated for durability, METIS functions reliably in extreme conditions such as ice, water, salt solutions, and other light-transmitting fluids without needing external circuitry. Additionally, we demonstrate passive control of analog switches via MQW diodes. The MQW diodes also enable contactless energy and optical information transfer, ensuring stable and controllable information reconstruction at the receiving end. This approach offers an innovative solution for energy and information transmission in extreme environments.

Biosynthesis, characterization of Ag2O nanoparticles for enhancement of antioxidant and photo degradation activities

Silver oxide (Ag2O) nanoparticles were synthesized via Biosynthesis methods using Croton macrotachyus leaf extract silver sulphate (Ag2SO4) was used as precursors and the extract was used in the reactions as reducing, stabilizing and capping agents which is a very easy and inexpensive synthesis method. Metal oxide nanoparticles characterized using UV–visible, TGA, Fourier transform infrared(FTIR),powder X-ray diffraction(XRD), X-ray photoelectron(XPS), and Scanning Electron Microscopy(SEM) with Energy Dispersive X-Ray(EDX) and Transmission Electron Microscopy(TEM), High Resolution Transmission Electron Microscope(HRTEM), and Selected Area Electron (SAED) which is used for determination of crystalline size, morphology,particle size and oxidation state with composition of elements in the sample. Metal oxide nanoparticles have a significant photo catalytic performance under visible light source confirmed that study supported Ag2O spherical structure, which exhibits enhanced photo catalytic activity and anti-oxidant activities as well as the size of the Ag2O nanoparticles was between 16 nm and 21 nm from the XRD(Scherrer equation),SEM & TEM) imaje software and Photo catalytic activity of Ag2O NPs were assessed against methylene blue (MB) dye degradation under natural sunlight illumination for 60 min. This sustainably photo degraded in direct solar irradiance with remarkable degradation percentage which is greater than 96.35 % and Scavenging activities Ag2O nanoparticles enhanced DDPH scavenging activities was 88 %

Corrigendum to “Determination of optimal transmission network constraints for novel close-to-real-time flexible energy market” [Energ. Rep., vol. 12, December 2024, Pages 1014–1024, doi.org/10.1016/j.egyr.2024.07.003

Corrigendum to “Determination of optimal transmission network constraints for novel close-to-real-time flexible energy market” [Energ. Rep., vol. 12, December 2024, Pages 1014–1024, doi.org/10.1016/j.egyr.2024.07.003

Cloud-Cyber Physical Systems: Enhanced Metaheuristics with Hierarchical Deep Learning-based Cyberattack Detection

Cyber-Physical Systems (CPS) integrate several interconnected physical processes, networking units, and computing resources, along with monitoring the processes of the computing system. The connection between the cyber and physical world creates threatening security problems, particularly with the growing complexities of transmission networks. Despite efforts to overcome this challenge, it remains challenging to analyze and detect cyber-physical attacks in CPS. This study mainly focuses on the development of Enhanced Metaheuristics with Hierarchical Deep Learning-based Attack Detection (EMHDL-AD) method in a cloud-based CPS environment. The proposed EMHDL-AD method identifies various types of attacks to protect CPS. In the initial stage, data preprocessing is implemented to convert the input dataset into a useful format. Then, the Quantum Harris Hawks Optimization (QHHO) algorithm is used for feature selection. An Improved Salp Swarm Algorithm (ISSA) is used to optimize the hyperparameters of the HDL technique to recognize several attacks. The performance of the EMHDL-AD algorithm was examined using two benchmark intrusion datasets, and the experimental results indicated improvements over other existing approaches.

On the hysteretic bending behavior of slack metallic cables

Short and slack stranded metallic cables are encountered in several technical applications to connect different structures or different parts of the same structural system. A couple of relevant examples are provided by flexible-bus conductors used in high-voltage electrical substations or passive damping devices installed on overhead electrical lines, such as Bretelle dampers. Bending behavior of metallic cables is nonlinear and non-holonomic due to the onset and propagation of relative sliding phenomena between the wires. While taut suspended cables have been widely studied in the literature under the classic assumptions of small sag and perfect flexibility, whenever dealing with short and slack cables, the aforementioned hypotheses typically cease to be valid, and the bending stiffness contribution plays a significative role in the determination of the overall structural response. In the present paper, two different modeling strategies for the description of the bending behavior of short and slack stranded cables are presented. The first (discrete bending model) describes the cable as a composite structural element made of wires, which are individually modeled as curved elastic thin rods interacting through normal and tangential contact forces. The second (phenomenological bending model), instead, relies on a description of the cable bending process based on an application of the well-known Bouc-Wen hysteresis model. Applications of the proposed models are presented both at the cross-sectional and at the structural levels. The systematic comparison between theoretical and experimental results allows to assess the performance of the two proposed bending models when used with “nominal” values of the model parameters, which are estimated only on the base of the knowledge of the material and geometric properties of the strand. Moreover, a back-analysis of the model parameters is carried out to get an insight on internal mechanical parameters of the cable and on the overall cross-sectional bending response (e.g. bending stiffness values).

Space-based solar power: Unlocking continuous, renewable energy through wireless transmission from space

Space-Based Solar Power (SBSP) is an emerging technology that aims to harness the abundant and uninterrupted solar energy available in space and beam it wirelessly to Earth. This innovative approach addresses the limitations of terrestrial solar energy, such as weather variability and the day-night cycle, by positioning solar power stations in space where sunlight is constant. The stations, composed of large arrays of solar panels, capture the solar energy and convert it into microwaves or laser beams, which are then transmitted to receiving stations on Earth for conversion into usable electricity. Research in SBSP focuses on optimal orbital positions, with geostationary and low Earth orbits being considered for maximum energy capture and efficient transmission. Advancements in materials, wireless power transmission technologies, and space-based assembly techniques are critical to the design and deployment of these stations. Additionally, economic and technical feasibility studies explore the challenges of launching, maintaining, and scaling SBSP systems. These include high initial costs, energy transmission efficiency, safety concerns, and the logistics of long-term maintenance. The potential benefits of SBSP are significant. It offers a clean, renewable energy source that could meet the growing global demand for electricity while reducing reliance on fossil fuels. By providing continuous energy, SBSP could enhance energy security and contribute to global efforts to combat climate change. However, several challenges remain, including addressing space debris risks and ensuring the safety of high-power energy transmission to Earth. As research and technological advancements continue, SBSP holds promise as a transformative solution for future energy needs.

Fabrication of Hydrophilic Organosilicon Coatings and Study of Their Resistance to Factors Accompanying Corona Discharge

Modern power industry widely uses high-voltage overhead lines to transport electricity, which are characterized by problems of corona discharge and leakage currents, especially in rain and snow conditions. One approach to solve these problems is to create protective coatings that can reduce corona in adverse weather conditions. This paper presents a study of a hydrophilic organosilicon coating based on aminopropyltriethoxysilane and PEG-400 for aluminum wires. Studies conducted to assess the resistance of the coating to prolonged contact with water, UV radiation and an ozone-saturated atmosphere have shown that the hydrophilicity of the coating increases under effect of these factors, which improves its anti-corona properties. Thus, durability under operating conditions open prospects for use of the developed coating in the energy sector.

Profiling vivax malaria incidence, residual transmission, and risk factors using reactive case detection in low transmission settings of Ethiopia

BackgroundIdentification of local Plasmodium vivax transmission foci and its hidden reservoirs are crucial to eliminating residual vivax malaria transmission. This study assessed whether reactive case detection (RCD) could better identify P. vivax cases and infection incidences in Arjo-Didessa, Southwestern Ethiopia.MethodsA RCD survey was conducted from November 2019 to October 2021 in Arjo-Didessa and the surrounding vicinity in southwestern Ethiopia. RCD was performed at 0, 30, and 60 days following reports of P. vivax infections by health facilities to detect further cases and potential transmission networks. Household members of the index case and neighbours living within 200 m of the index household were screened for P. vivax. Households 200–500 m away are considered controls and were also screened for P. vivax. Plasmodium vivax was detected by microscopy, rapid diagnostic testing (RDT), and quantitative polymerase chain reaction (qPCR). Risk factors associated with vivax malaria were analysed using generalized estimating equations (GEE).ResultsA total of 3303 blood samples were collected from the index (n = 427), neighbouring (n = 1626), and control (n = 1240) household in the three rounds of follow-up visits for malaria infection, the overall positivity rate of P. vivax malaria was 1.6% (95% CI 1.2–2.2%), 1.9% (95% CI 1.5–2.4), and 3.9% (95% CI 3.2–4.6%) by microscopy, RDT, and qPCR, respectively. Microscopy and RDT detected 41.5% (54 of 130) and 49.1% (64 of 130) of the qPCR-confirmed P. vivax cases, respectively. Of qPCR-positive samples, 77.7% of the total P. vivax infections circulated in the index and neighbouring households, while control households accounted for 23.3% of the infections. Of the P. vivax infections detected 81.0% (95% CI 72.9–87.1%) were asymptomatic. In this study, P. vivax infection incidence was higher in index case households (53.8 cases per 1000 person-months) and (44.0 cases per 1000 person-months) in neighbouring households compared to the control households (25.1 cases per 1000 person-months) with statistical difference (p = 0.02). In index case households, children < 5 years and school-age children were at higher risk of P. vivax infection (AOR: 6.3, 95% CI: 2.24–18.02, p = 0.001 and AOR: 2.7, 95% CI: 1.10–6.64, p = 0.029).ConclusionsThis study found clustering of asymptomatic and sub-microscopic P. vivax infections in the index case household and their neighbours using RCD and molecular methods. Children under 5 years and of school age were more likely to have P. vivax infection in index households. Thus, tailored RCD approaches and targeted interventions for interrupting residual P. vivax transmission networks are needed to eliminate P. vivax malaria in low transmission settings.

Distributed voltage control for multi-feeder distribution networks considering transmission network voltage fluctuation based on robust deep reinforcement learning

In the multi-feeder distribution network, the power balance between photovoltaics generations and load demands across regions is more complex. To solve the above problems, this paper proposes a multi-agent distributed voltage control strategy based on robust deep reinforcement learning to reduce voltage deviation. The whole multi-feeder distribution network is divided into a main agent and several sub-agents, and a multi-agent distributed voltage control model considering the transmission network voltage fluctuations and the corresponding power fluctuations is established. Based on the information uploaded by sub-agents, the main agent models the uncertainty of the transmission network voltage fluctuations and the corresponding power fluctuations as a disturbance to the state, and a RDRL method is employed to determine the tap position of on-load tap changer. Furtherly, each sub-agent uses the second-order cone relaxation technique to adjust the reactive power outputs of the inverters on each feeder. The effectiveness of the proposed method has been verified in two real-world multi-feeder systems. The results show that the proposed method can achieve millisecond-level decision-making, with a voltage deviation only 1.28 % higher than the global optimal results, achieving near-optimal control. The proposed method also demonstrates robustness in handling transmission network uncertainties and partial measurement loss.

A TOPSIS based multi-objective optimal deployment of solar PV and BESS units in power distribution system electric vehicles load demand

The growing concerns regarding the depletion of fossil fuels, CO2 emissions, and the effects of climate change prompt the usage of plug-in electric vehicles (PHEVs) all over the world in a big way. The increased electrical demand brought on by the charging of electric vehicles puts a burden on the distribution network parameters like energy loss, voltage profile and thermal limits. Recently, renewable energy-based distribution generation (RDGs) units are firmly integrated with the transmission and distribution system networks to lower the carbon footprint generated due to conventional thermal power plants. In addition, Battery Energy Storage Systems (BESS) are used to enhance grid operation and lessen the consequences of the high intermittency nature of RDGs power. In this work, two charging methods of PHEVs are considered: charging electric vehicles at home during night-time and charging electric vehicles at public fast charging stations (PFCS). The uncertain nature of arrival time and trip distance of PHEVs are addressed using probability density functions (PDFs). The 33-bus test system consists of commercial, industrial and residential buses is taken to implement the proposed methodology. In this work, 500 PHEVs are taken into consideration. The aforementioned charging methods produce a 24-h electric demand for PHEVs, which is then placed on the corresponding distribution system buses. The effect of PHEVs on technical distribution system metrics, including voltage profile and energy loss, is investigated. To improve the above metrics, optimal planning of inverter-based non-dispatchable PV units and dispatchable PV-BESS units in the distribution network by the inclusion of PHEVs electric load demand is addressed. The Pareto-based meta-heuristic multi-objective chaotic velocity-based butterfly optimization method (MOCVBOA) is chosen for optimization of desired objectives. The results of the MOCVBOA optimization algorithm are compared with those of the other optimization algorithms, NSGA-II & MOBOA, frequently described in the literature to assess its effectiveness.

Resistome phylodynamics of multidrug-resistant Shigella isolated from diarrheal patients.

Multi-drug resistance (MDR) in Shigella continues to pose a significant public health challenge, particularly in developing countries. Recent advances in genomics strengthen the surveillance of MDR-pathogens and antimicrobial resistance (AMR) mediators. However, genome-based investigations into resistome dynamics in Shigella are limited, specifically in Bangladesh. Therefore, we investigated MDR-Shigella resistomes to evaluate their AMR transmission and phylodynamics. Clinical Shigella strains were screened for MDR phenotypes through susceptibility tests against 28 antibiotics from 10 different classes. Whole-genome sequencing (WGS) and bioinformatics approaches were performed to unveil the resistome dynamics: >500 global plasmid entities and >1,000 plasmid-mediated resistance gene clusters from global databases were included in this study. We identified 28 distinct antimicrobial resistance genes (ARGs) from nine antibiotic classes, with 75% originating from plasmids. Notably, two conjugative MDR plasmids included nearly all potential ARGs, conferring resistance to first-line drugs for shigellosis. Two third-generation cephalosporin-resistant [wubC-blaCTX-M-15-ISEcp1 and blaTEM-1] and two macrolide-resistant mobile genomic islands (GIs) [mphA-mrx-mph(R)A-IS6100 and mphE-msrE-IS482-IS6] had emerged in Shigella in Bangladesh. In addition, trimethoprim-aminoglycoside-streptothricin-sulfonamide-resistant dfrA1-sat1-aadA1 and aph3-dfrA14-aph6-sul2 were in conjugative plasmids in Bangladesh. The MDR plasmids and resistant GIs were phylogenetically relevant to Europe, USA, or China-derived isolates, indicating carry-over of the emerging ARGs from heavily industrialized countries and MSM-burdened (men who have sex with men) populations. The global burden of resistance GIs has increased sharply, especially after 2014. Emerging resistance mediators were most frequent (>80%) in human-associated Escherichia coli and Klebsiella pneumoniae. We infer ARGs horizontally propagate among Enteropathogens: informing treatment strategies and supporting policymakers in strengthening AMR-containment efforts utilizing the phylodynamics network.IMPORTANCEThe world is suffering from a high burden of MDR enteropathogens. Healthcare providers in low- and middle-income countries (LMICs) often face trouble finding effective drugs among the many antibiotics introduced in diarrheal treatment. Resistance-mediated drug inactivation is more rapid than the advent of new antimicrobials, leaving enteritis treatment on the edge. In Bangladesh, where one-third of users are self-prescribing antibiotics and thousands are dying due to resistance-related treatment failure, phylogenomic evidence of AMR transmission root is scarce. Therefore, investigating the resistomes of MDR-Shigella, the leading cause of diarrheal deaths in Bangladesh, is crucial. We identified several emerging resistance mediators and their phylogenetic links to global entities, which is significant for improving shigellosis treatment and enhancing AMR containment strategies. Understanding the MDR mechanism in Shigella will help physicians choose effective drugs and anticipate resistance-mediated changes in treatment approaches; the spatiotemporal phylodynamics of AMR mediators aid policymakers in setting effective checkpoints in the AMR transmission network.