Discover insights from thousands of peer-reviewed papers on microbial electrochemical systems
Sumiati, M. Akmal Surur
BIOEDUSCIENCE · 2021
Background: Hydroponics is a method of agriculture that utilizes water as a planting medium. The purpose of the study was to find out good working procedures and nutrient solution formulas capable of showing symptoms of morphological nutritional deficiencies in plants. Methods: The plants used are the seeds of kale plants that are sown within 1 - 2 weeks (until roots, stems, and leaves grow). The design used is Random Group using three formulas with nine kinds of treatment on each formula and three repeats. Experiments were conducted on two hydroponic systems, the axis and without the axis. The parameters observed are the number of leaves, the leaves' length, the leaves' width, the plant's height, and the plant's height. Results: Observational data in the analysis using SPSS 25. The effect of treatment with test parameters is seen using ANOVA analysis and BNT advanced tests. Visual observations showed the formula of nutrient solutions 1, 2 and 3 using both the axis system and without the axis showed symptoms of nutritional deficiencies in kale plants. The axis less system experiment gave more significant results on all parameters except PD (leaf length) than the axis system based on the average results of BNT advanced tests. Conclusion: Nutrient solutions 1, 2 and 3 have a real effect on all test parameters.
Nataliia Volosnikova
Bulletin of the National Technical University "Kharkiv Polytechnic Institute" (economic sciences) · 2023
The article is devoted to the study of the relationship between logistics synergy and the security system as important factors for ensuring sustainable development in the context of global threats. The concept of logistics synergy is considered as an effective interaction and coordination of various logistics elements in order to achieve a total positive impact. The main focus of the article is aimed at analyzing the role of security systems in ensuring the sustainability of development in conditions of global dangers. It is studied how an effective security system can affect logistics processes, ensuring their reliability and resistance to external influences. The practical aspects of implementing logistics synergy and security systems into the practice of management in conditions of global risks are also considered. Emphasis is placed on the importance of developing integrated strategies aimed at a balanced combination of logistics efficiency and security to achieve sustainable development. An analysis of the important interrelationships between logistical synergies and security aspects is presented, as their integration can become a key factor in ensuring not only efficiency, but also sustainability in the face of global challenges. Various models and strategies aimed at the interaction between logistics processes and security systems are given, in particular, in the context of addressing uncertainty and risks arising from global hazards. An important emphasis is placed on the need to develop integrated strategies that cover both aspects of optimizing logistics processes and systems for warning and responding to possible threats. The author offer practical recommendations for implementing an integrated approach to managing logistics and security in the face of global uncertainty. The conclusions of the article can be useful for logistics specialists, researchers in the field of security and sustainable development, as well as for practicing managers who seek to optimize their strategies in the face of complex global conditions.
Anuradha Tomar
Recent Patents on Engineering · 2020
Background: Despite so many developments, most of the farmers in the rural areas are still dependent on rainwater, rivers or water wells, for irrigation, drinking water etc. The main reason behind such dependency is non-connectivity with the National grid and thus unavailability of electricity. To extract the maximum power from solar photovoltaic (SPV) based system, implementation of Maximum Power Point Tracking (MPPT) is mandatory. PV power is intermittent in nature. Variation in the irradiation level due to partial shading or mismatching phenomena leads to the development of modular DC-DC converters. Methods: A stand-alone Multi-Input Dual-Output (MIDO) DC-DC converter based SPV system, is installed at a farm; surrounded with plants for water pumping with stable flow (not pulsating) along with battery energy storage (BES) for lighting. The proposed work has two main objectives; first to maximize the available PV power under shadowing and mismatching condition in case of series/ parallel connected PV modules and second is to improve the utilization of available PV energy with dual loads connected to it. Implementation of proposed MIDO converter along with BES addresses these objectives. First, MIDO controller ensures the MPPT operation of the SPV system to extract maximum power even under partial shading condition and second, controls the power supplied to the motor-pump system and BES. The proposed system is simulated in MATLAB/ SIMULINK environment. Real-time experimental readings under natural sun irradiance through hardware set-up are also taken under dynamic field conditions to validate the performance. Results and Conclusion: The inherent advantage of individual MPPT of each PV source in MIDO configuration, under varying shadow patterns due to surrounding plants and trees is added to common DC bus and therefore provides a better impact on PV power extraction as compared to conventional PV based water pumping system. Multi-outputs at different supply voltages is another flag of MIDO system. Both these aspects are implemented and working successfully at 92.75% efficiency.
Herlambang Setiadi, Mithulananthan Nadarajah, Md Rakibuzzaman Shah et al.
Preprints.org · 2020
This paper proposed a damping method for enhancing oscillatory stability performance of power systems with high penetration of renewable energy by a resilient wide-area multi-mode controller. The resilient wide-area multi-mode controller is used as an additional controller in a renewable energy system with a battery energy storage to enhance the damping of the critically weak modes. The weak modes are likely to be triggered in the event of line outages or any other disturbances, and the system may become unstable in the absence of proper corrective and preventive control. A firefly algorithm has been employed to design such a controller. Eigenvalue analysis and time-domain simulation are used to analyze the performance of the proposed controller in a realistic representative power system. From the simulation results, it is evident that the oscillatory stability performance of the renewable rich power system can be enhanced with the proposed control to keep the damping on critical modes to the industrial standards. Furthermore, renewable energy penetration can be increased significantly in the realistic representative system by introducing the proposed controller without disturbing the oscillatory stability margin.
Francisca Font-Verdera, Raquel Liébana, Ramon Rossello-Mora et al.
FEMS Microbiology Ecology · 2023
Abstract Sediments underlying the solar salterns of S’Avall are anoxic hypersaline ecosystems dominated by anaerobic prokaryotes, and with the especial relevance of putative methanogenic archaea. Slurries from salt-saturated sediments, diluted in a gradient of salinity and incubated for > 4 years revealed that salt concentration was the major selection force that deterministically structured microbial communities. The dominant archaea in the original communities showed a decrease in alpha diversity with dilution accompanied by the increase of bacterial alpha diversity, being highest at 5% salts. Correspondingly, methanogens decreased and in turn sulfate reducers increased with decreasing salt concentrations. Methanogens especially dominated at 25%. Different concentrations of litter of Posidonia oceanica seagrass added as a carbon substrate, did not promote any clear relevant effect. However, the addition of ampicillin as selection pressure exerted important effects on the assemblage probably due to the removal of competitors or enhancers. The amended antibiotic enhanced methanogenesis in the concentrations ≤ 15% of salts, whereas it was depleted at salinities ≥ 20% revealing key roles of ampicillin-sensitive bacteria.
N. Pous, C. Koch, A. Vilà-Rovira et al.
RSC Advances · 2015
Elucidating the structure–function relationship of a denitrifying biocathodes.
Shi‐Hui Si, Li‐Hua Nie, Shou‐Zhuo Yao
Chinese Journal of Chemistry · 1994
Abstract Based on the impedance behavior of red cell at high frequency, the frequency response of series piezoelectric crystal sensor in the red cell suspension was derived and verified experimentally. A method of using piezoelectric crystal sensor to determine the conductivity of the interior of the cell was proposed. The experimental results show that the mean conductivity of rabbit red cell cytoplasm was 0.269 S/m and the mean shape factor of red cell was 2.05.
K. L. Sebastian
The Journal of Chemical Physics · 1989
The transfer of an electron from a metal electrode to an ion in a polar liquid is probably the most important process in electrochemistry. As the electrons in the metal have a continuum of allowed energy levels, this transfer may be accompanied by the creation/annihilation of electron–hole excitations in the metal. Calculation of the rate of electron transfer, with these excitations and the solvent accounted for properly is a difficult problem to treat using the usual approaches of molecular reaction dynamics, as one now has a continuum of potential energy surfaces, on which the dynamics has to be considered. We suggest an approach in which the electron–hole excitations are treated as bosons. Using this, we have derived an expression for the rate, which accounts both for solvent dynamics and electron–hole excitations. Our analysis amounts to a solution of the problem of calculating the electronic transmission coefficient κ, for a continuum of crossing diabatic surfaces. Calculations of the rate using our expression are reported.
Joanna Rogińska, Timothé Philippon, Morgane Hoareau et al.
Bioelectrochemistry · 2023
Aliyu Ishaq, Mohd Ismid Mohd Said, Shamila Azman et al.
Environmental Science and Pollution Research · 2023
Microbial fuel cells (MFCs) have garnered attention in bio-electrochemical leachate treatment systems. The most common forms of inorganic ammonia nitrogen are ammonium ( NH 4 + ) and free ammonia. Anaerobic digestion can be inhibited in both direct (changes in environmental conditions, such as fluctuations in temperature or pH, can indirectly hinder microbial activity and the efficiency of the digestion process) and indirect (inadequate nutrient levels, or other conditions that indirectly compromise the microbial community's ability to carry out anaerobic digestion effectively) ways by both kinds. The performance of a double-chamber MFC system-composed of an anodic chamber, a cathode chamber with fixed biofilm carriers (carbon felt material), and a Nafion 117 exchange membrane is examined in this work to determine the impact of ammonium nitrogen ( NH 4 - N ) inhibition. MFCs may hold up to 100 mL of fluid. Therefore, the bacteria involved were analysed using 16S rRNA. At room temperature, with a concentration of 800 mg L -1 of ammonium nitrogen and 13,225 mg L -1 of chemical oxygen demand (COD), the study produced a considerable power density of 234 mWm -3 . It was found that NH 4 - N concentrations above 800 mg L -1 have an inhibitory influence on power output and treatment effectiveness. Multiple routes removed the most nitrogen ( NH 4 + -N: 87.11 ± 0.7%, NO 2 - N: 93.17 ± 0.2% and TN: 75.24 ± 0.3%). Results from sequencing indicate that the anode is home to a rich microbial community, with anammox (6%), denitrifying (6.4%), and electrogenic bacteria (18.2%) making up the bulk of the population. Microbial fuel cells can efficiently and cost-effectively execute anammox, a green nitrogen removal process, in landfill leachate.
Franc Paré, Mingyue Pan, Oscar Guerrero‐Sodric et al.
ChemElectroChem · 2025
As a key parameter, pH has received a lot of attention from the sensing perspective. New materials and technologies are being used to produce state of the art devices capable of tracking it. However, most generic sensors lack the applicability that certain applications require. In this article, 3D printing technology is used to its full potential to produce electrodes, which are modified into pH sensors and reference electrodes, with shapre‐driven additional functionality for monitoring ammonia recovery in a bioreactor. The 3D‐printed electrodes are modified with a layer of iridium oxide to be turned into pH sensitive devices. Their characterization showed their characteristic super‐Nernstian response (−77 ± 0.2 mV pH −1 ), high reproducibility (RSD < 5%) between sensors and repeatability (RSD < 2%) between measurements. Moreover, the sensors are stable for at least 20 days and tunable in length. All of this results in the sensors being built into a functional shape and tested to monitor the performance of an ammonia‐producing bioelectrochemical reactor.
Nadali Alavi, Monireh Majlessi, Nazak Amanidaz et al.
MethodsX · 2022
Many researchers are interested in utilizing renewable and sustainable energy made by exoelectrogenic bacteria during electrodialysis for the separation of minerals and organic matters from aqueous environments. The aim of this study was to develop a novel thermophilic fermenter and dual anion exchange membrane bioelectrochemical system for separating biohydrogen production inhibitors from the thermophilic fermenter and thereby increasing biological and cathodic hydrogen production by food waste and wastewater.•Using this innovative system the biohydrogen production inhibitors were separated and nutrients (for example ammonium), alkalinity, buffering capacity and pH were preserved in the bioreactor at the same time, led to higher biological and cathodic hydrogen production.
P. Kirmizakis, R. Doherty, C. Mendonça et al.
Environmental Science and Pollution Research · 2019
Here, we show the electrical response, bacterial community, and remediation of hydrocarbon-contaminated groundwater from a gasworks site using a graphite-chambered bio-electrochemical system (BES) that utilizes granular activated carbon (GAC) as both sorption agent and high surface area anode. Our innovative concept is the design of a graphite electrode chamber system rather than a classic non-conductive BES chamber coupled with GAC as part of the BES. The GAC BES is a good candidate as a sustainable remediation technology that provides improved degradation over GAC, and near real-time observation of associated electrical output. The BES chambers were effectively colonized by the bacterial communities from the contaminated groundwater. Principal coordinate analysis (PCoA) of UniFrac Observed Taxonomic Units shows distinct grouping of microbial types that are associated with the presence of GAC, and grouping of microbial types associated with electroactivity. Bacterial community analysis showed that β-proteobacteria (particularly the PAH-degrading Pseudomonadaceae) dominate all the samples. Rhodocyclaceae- and Comamonadaceae-related OTU were observed to increase in BES cells. The GAC BES (99% removal) outperformed the control graphite GAC chamber, as well as a graphite BES and a control chamber both filled with glass beads.
Gabriele Beretta, Matteo Daghio, Anna Espinoza Tofalos et al.
Water · 2019
Chromium is one of the most frequently used metal contaminants. Its hexavalent form Cr(VI), which is exploited in many industrial activities, is highly toxic, is water-soluble in the full pH range, and is a major threat to groundwater resources. Alongside traditional approaches to Cr(VI) treatment based on physical-chemical methods, technologies exploiting the ability of several microorganisms to reduce toxic and mobile Cr(VI) to the less toxic and stable Cr(III) form have been developed to improve the cost-effectiveness and sustainability of remediating hexavalent chromium-contaminated groundwater. Bioelectrochemical systems (BESs), principally investigated for wastewater treatment, may represent an innovative option for groundwater remediation. By using electrodes as virtually inexhaustible electron donors and acceptors to promote microbial oxidation-reduction reactions, in in situ remediation, BESs may offer the advantage of limited energy and chemicals requirements in comparison to other bioremediation technologies, which rely on external supplies of limiting inorganic nutrients and electron acceptors or donors to ensure proper conditions for microbial activity. Electron transfer is continuously promoted/controlled in terms of current or voltage application between the electrodes, close to which electrochemically active microorganisms are located. Therefore, this enhances the options of process real-time monitoring and control, which are often limited in in situ treatment schemes. This paper reviews research with BESs for treating chromium-contaminated wastewater, by focusing on the perspectives for Cr(VI) bioelectrochemical remediation and open research issues.
Tahmineh Taheri Dezfouli, M. Kashefiolasl, R. Marandi et al.
SHILAP Revista de lepidopterología · 2020
The modern bio-electrochemical technologies can convert the energy stored in the chemical bonds of biodegradable organic materials into renewable electrical bioenergy through the catalytic reactions of the microorganisms while treating the wastewaters. The present research has been conducted to study the efficiency of the single-chamber bio electrochemical system with carbon aerogel catalyst as a new, simple and inexpensive approach to remove and recover the valuable but polluting nutrients (nitrogen and phosphorus) from municipal wastewaters and also determines the optimal conditions to scale up the system in countries with hot, dry climates. In the present study, the bacterial consortium was isolated from the sediments of local lagoons, and the municipal wastewater was used as the substrate. During the six months of cell operation, the effluent of BES showed a 54.9% decrease in nitrate concentration and a 59.8% decrease in total N and 90% of phosphate removed from wastewater, the total nitrogen and total phosphate concentration in effluent were 28.9 ± 24.3 mg/l. and 13 ± 46.8 mg/l, respectively. The maximum removal of COD was 80%, and the maximum power density was 1.82mW/m2. Carbon aerogel as a novel material with suitable absorbance and resistance to oxidation by urban wastewater pH can be coated on electrodes to facilitate the Oxidation Reduction reactions and electricity transmission
M. de la Fuente, Carlos Gallardo-Bustos, R. De la Iglesia et al.
International Journal of Environmental Research and Public Health · 2022
For many years, the world’s coastal marine ecosystems have received industrial waste with high nitrogen concentrations, generating the eutrophication of these ecosystems. Different physicochemical-biological technologies have been developed to remove the nitrogen present in wastewater. However, conventional technologies have high operating costs and excessive production of brines or sludge which compromise the sustainability of the treatment. Microbial electrochemical technologies (METs) have begun to gain attention due to their cost-efficiency in removing nitrogen and organic matter using the metabolic capacity of microorganisms. This article combines a critical review of the environmental problems associated with the discharge of the excess nitrogen and the biological processes involved in its biogeochemical cycle; with a comparative analysis of conventional treatment technologies and METs especially designed for nitrogen removal. Finally, current METs limitations and perspectives as a sustainable nitrogen treatment alternative and efficient microbial enrichment techniques are included.
Farah Syakinah Md Sokor, Ryan Yow Zhong Yeo, Manal Ismail et al.
Jurnal Kejuruteraan · 2024
Microbial Electrochemical Sensor (MES) is classified as a self-powered electrochemical biosensor and utilising biofilm electrode used as a sensor. Conventional techniques such as ion chromatography (IC), highperformance liquid chromatography (HPLC), and chemical oxygen demand (COD) require large, expensive, and non-portable equipment. Therefore, MES has gained significant attention due to its advantages, including low cost, easy operation, and portability. Stainless steel (SS404L) is widely used in various applications but exhibits lower biocompatibility compared to carbon-based materials when used as an anode. This MES is constructed using modified electrodes through techniques such as carbon coating, electrochemical reduction, flame oxidation, and graphene coating. A nutrient medium stock is periodically supplied daily to enrich the electroactive microbes and maintain the biosensor’s performance at a stable level. Samples are collected and analyzed during pollutant testing to determine pH, conductivity, IC, and COD values. These parameter values are correlated with MES signal data to assess each biosensor’s performance in terms of reaction rate. FESEM-EDX and RAMAN analysis indicated an increase in O content from 2.4% to 19.9% for flame oxidation. For carbon and graphene coatings, carbon content increased from 4.1-9.1% to 48.4-78.6%. The carbon coating exhibited an R² value of 0.7023 for tests using 4-nitrophenol. Meanwhile, for potassium nitrate, the graphene coating demonstrated the highest R² value of 0.8785. A higher R² value indicates a stronger correlation and suggests better performance in electrode modification within MES. This highlights the significance of MES results in water quality monitoring, which can be understood and applied more effectively.
Jiyuan Li, Jie Zhou, Wenping Cao et al.
Water · 2022
In order to study the effects of operating conditions on the performance of a microbial fuel cell (MFC) for treating ammonia nitrogen (NH4+-N) and phosphate and the changes in the microbial community under optimized conditions, in this study, the response surface method (RSM) and central composite design (CCD) were used to carry out experiments and construct a model of the system to analyze the influence of the hydraulic retention time (HRT) and initial influent ammonia concentration on NH4+-N and the total phosphorus (TP) removal performance of the MFC, and the changes in the microbial community structure were analyzed. The results showed that: (1) the initial influent ammonia concentration had a greater impact than the HRT; (2) after optimizing the reaction conditions, the actual removal rates of NH4+-N and TP of the system were 94.88% and 59.39% (the predicted values were 90.18% and 56.25%), respectively; and (3) the total number of species in the optimization group decreased, and the richness of the microbial community decreased. The system conducted the orthoselection of the microbial community and optimized the structure of the microbial community. After the optimization, the dominant strains for ammonia and phosphorus removal on the cathode reactor of each system were strengthened at the phylum and genus levels. Under the coaction of the dominant strains, the efficiencies of nitrogen removal and phosphorus removal in the reactor were significantly improved. The performance optimization of and microbial community change in NH4+-N and TP removal in the MFC system were studied using RSM, which was helpful to improve the effect of nitrogen and phosphorus removal.
Thais Gonzalez, Juan Pablo Miranda, Gladys Vidal
Global NEST International Conference on Environmental Science & Technology · 2022
The combination of constructed wetlands (CWs) and microbial fuel cells (MFCs) has emerged in recent years with the purpose of enhancing wastewater treatment efficiency of CWs while simultaneously generating electricity. Taking the above into account, the aim of this study is to evaluate the influence of MFC integration on organic matter and nutrient removal in a constructed wetland. The results showed that NH4+-N concentration reduced from 66.5 ±9.4 at day 0 (influent) to 4.5±0.4 mg/L and 7.03±3.93mg/L for the integrated system and control system, respectively. In terms of the NH4+-N removal efficiency, an enhancement of the nitrification rate (Ni) was observed when MFC was integrated in VSSF (120.6±1.5 mg/m2 d for control system and 166.8±2.3 mg/m2 d for integrated system). The average COD removal efficiencies were 89.36±2.67% and 94.2±5.9% in the CW and CW-MFC, respectively, obtaining a voltage close to 250 mV. The maximum power density generated was 4.75 mW/m2. In conclusion, the removal efficiencies of COD and NH4+-N in VSSF were 89.4 and 88.4%, respectively, while in CW-MFC were 94.3 and 93.2 %. Therefore, the integrating of a MFC into CW does not have adverse effects on the capacity of the CW to efficiently domestic wastewater treatment.
Vinothkumar Veeramani, Kanimozhi Rajangam, Jaya Nagendran
Sustainable Environment Research · 2020
Abstract The use of non-noble metal catalyst as electrode for energy harvesting device have drawn great deal of attention owing to its distinct features. In this work, cobalt oxide has been directly fabricated on carbon cloth substrates using simple cost effective Successive Ionic Layer Adsorption and Reaction. Cobalt oxide synthesized from Co (II) nitrate and NaOH was used as the electrode for generation of electricity from dairy wastes using Microbial Fuel Cells (MFC). Electrochemical characteristics such as cyclic voltammetry have been carried out for the cobalt oxide/carbon cloth and the obtained results are found to be a good alternative for platinum catalyst. A current of 0.15 mA was obtained at an external resistance of 2 kΏ. A single cell prototype of double chamber MFC is designed and the performance analysis is carried out in this work.
Shamsuddeen Jumande Mohammad, Aliyu Ishaq
Research Square · 2024
Abstract This research presents an innovative blend of feature selection and sensitivity analysis techniques, which is an essential yet overlooked aspect in the study of MFCs. The study compared predictive models utilizing various machine learning algorithms to assess the impact of time, dosage, pH and temperature on ammonium nitrogen concentration (NH 4 -N) to predict the power density (PD) output of microbial fuel cells using leachate as a substrate for treatment. Evaluation of six machine learning models demonstrates varying levels of predictive accuracy. CatBoost (R2:0.9969, MSE: 48.8430, RMSE:6.9888) emerges as the most accurate model, followed closely by XGBoost (R2:0.9917, MSE:130.1668, RMSE:11.4091) and Random Forest (R2:0.9830, MSE:267.0929, RMSE:16.3430). Time series plots illustrate the performance of different models in predicting PD over a period, indicating good alignment with observed data. Comparison of Mean Squared Error (MSE) highlights significant variations in prediction accuracy, with CatBoost demonstrating the greatest enhancement and precision. The study directly tackles the deficiencies in existing MFC predictive modeling by incorporating the CatBoost algorithm, which provides enhanced accuracy and a deeper understanding of the nonlinear connections between environmental variables and power density.
Diplina Paul, Abhisek Banerjee
Nitrogen · 2022
Water contamination due to various nitrogenous pollutants generated from wastewater treatment plants is a crucial and ubiquitous environmental problem now-a-days. Nitrogen contaminated water has manifold detrimental effects on human health as well as aquatic life. Consequently, various biological treatment processes are employed to transform the undesirable forms of nitrogen in wastewater to safer ones for subsequent discharge. In this review, an overview of various conventional biological treatment processes (viz. nitrification, denitrification, and anammox) have been presented along with recent novel bioelectrochemical methods (viz. microbial fuel cells and microbial electrolysis cells). Additionally, nitrogen is an indispensable nutrient necessary to produce artificial fertilizers by fixing dinitrogen gas from the atmosphere. Thus, this study also explored the potential capability of various nitrogen recovery processes from wastewater (like microalgae, cyanobacteria, struvite precipitation, stripping, and zeolites) that are used in industries. Further, the trade-offs, challenges posed by these processes have been dwelt on along with other biological processes like CANON, SHARON, OLAND, and others.
Kritika Singh, Minni Jain
Research Square · 2025
Abstract Crying is one of the most fundamental ways an infant can communicate with the outside world. The cry contains vital information to determine the needs of the baby, whether due to hunger, pain, fatigue, or simply discomfort [1]. The accurate interpretation of these subtle acoustic patterns carried by cry signals is crucial for proper care and early diagnosis. This study presents an innovative approach to infant cry classification using explainable reinforcement learning and feature fusion methods. We dynamically assign different attention weights to already extracted features using a lightweight policy agent learned via the REINFORCE algorithm [2]. The model is trained and validated on a widely and popular literature dataset named Donate-a-Cry Corpus , which classifies cries into five categories namely; hunger, tiredness, belly pain, burping, and discomfort. In order to help reduce the extreme class imbalance present in the dataset, we use specific data augmentation methods. We also introduce a dynamic reward shaping mechanism into the reinforcement loop that improves the agent’s ability to focus on underrepresented classes. Once augmented and balanced, most salient acoustic features (MFCC, GFCC and prosodic features) are extracted and processed using a lightweight MLP(Multi-layer Perceptron) classifier for final classification. To validate our model, we apply k=3-fold cross-validation where we achieve an accuracy of 94.44%.
Hongyan Yuan, Zhendong Sun, Yujie Wang et al.
· 2022
In a proton exchange membrane fuel cell (PEMFC) system, the flow of the air and hydrogen is the main factor affecting the output characteristics of the PEMFC, and there is a coordination problem in the flow control of both. To ensure the real-time gas supply in the fuel cell and improve the output power and economic benefits of the system, a deep reinforcement learning (DRL) controller based on fusion optimization with deterministic policy gradient and a control optimization strategy based on net power optimization are proposed in this paper. The experimental results show that the control algorithm proposed in this paper can effectively improve the dynamic performance and steady-state performance of the system, which is embodied in the average 12.5% increase in the dynamic performance compared with the fuzzy PID control and average 99.54% increase in the steady-state performance compared with the traditional DRL control.
Hui Li, Jun Zhang, Jianhua Hong et al.
Research Square · 2026
Abstract To address the issues of detail loss and matching difficulties in fruit tree 3D reconstruction caused by complex branch–leaf morphology, fruit occlusion, and illumination variations, this paper proposes an end-to-end cross-scale collaborative attention multi-view stereo network, termed MSA-MVSNet, for high-quality 3D reconstruction of orchard trees, while integrating semantic segmentation for fruit counting. A multi-scale feature enhancement module is designed to adaptively fuse deep semantic features and shallow fine-grained details through a spatial–channel collaborative attention mechanism, thereby enhancing the network’s capability to represent multi-scale structures such as trunks, branches, and leaves. Multi-branch dilated convolutions are introduced to enlarge the receptive field, and deformable convolutions are incorporated to adaptively capture the irregular geometric shapes of fruits, improving modeling robustness. In addition, a feature matching transformer is introduced to strengthen long-range global contextual correlations within and across images via intra-attention and inter-attention mechanisms, thereby improving matching stability in low-texture and repetitive-texture regions.To validate the effectiveness of the proposed method, experiments are conducted on self-collected real orchard dataset and public benchmark datasets. The results demonstrate that MSA-MVSNet outperforms baseline models by 8.2% in terms of 3D reconstruction quality. Finally, by combining depth filtering with the semantic segmentation results of YOLOv11-Seg, a semantic-guided fruit reconstruction and counting framework is constructed. This framework achieves an overall counting F1-score of 92.8% on the self-collected dataset with varying scene sparsity and 93.5% on the public Fuji-sfm dataset, demonstrating its effectiveness and generalization capability.
Jiao Xue, Ying Zhang, Hongyun Zhao et al.
Frontiers in Pediatrics · 2026
Introduction Gitelman syndrome (GS) presents with a broad range of clinical manifestations. Although uncommon, seizures secondary to severe metabolic alkalosis or hypomagnesemia have been documented. A concurrent diagnosis of epilepsy in patients with GS is even rarer. Case presentation We report the case of a 12-year-old boy whose chief complaint was recurrent convulsions. Initial laboratory evaluation revealed normal serum magnesium levels, which subsequently decreased during follow-up. Persistent hypokalemia, hyperaldosteronism, and hypomagnesemia in subsequent disease course, as well as mutations of the SLC12A3 gene, confirmed the diagnosis of GS. Based on long-term monitoring of seizure episodes, electroencephalogram findings, and the electrolyte levels during an epileptic seizure, a diagnosis of epilepsy was established. His seizures were well controlled with levetiracetam. Conclusion We report a case of GS presenting with convulsions as the chief complaint. The etiology of epilepsy in this case remains unclear and may represent either a causal association or a coincidental comorbidity with GS. The mechanism of the atypical dynamics of serum magnesium levels in this patient—normal levels initially followed by a subsequent decrease—warrants further investigation.
Yinghui Meng, Jiaoshuai Song, Duan Li et al.
Information · 2026
Cross-subject emotion recognition using EEG remains challenging due to substantial inter-individual variability. To address this, we propose a Multi-scale Spatio-Temporal Convolution and Multi-order Gated Spatial-Channel Aggregation Network (MCF-SCA). The model leverages multi-scale spatio-temporal convolution to capture rich temporal and spatial features and applies Fast Fourier Transform to transform EEG signals into the frequency domain, enhancing emotion-related representations. A multi-order spatial-channel aggregation module is then introduced, which adaptively integrates features across spatial and channel dimensions through a gating mechanism, enabling dynamic feature weighting and more expressive emotional representations. Experiments on the DEAP dataset show accuracy gains of up to 11–30% for arousal and 12–31% for valence compared with TSception, CNN, LSTM, EEGNet, and MLP. On the DREAMER dataset, improvements reach 5–33% and 3.7–34%, respectively. These results confirm that MCF-SCA achieves superior accuracy and cross-subject adaptability, providing strong support for emotion-based brain–computer interface applications.
Mengxiong Lv, Chenchen Zhang, Ling Shi et al.
Machines · 2026
Piston pumps are core components in hydraulic systems, and their performance, efficiency, and stability significantly impact the operation of the entire system. The flow distribution method is a key factor determining the overall performance of the piston pump, directly affecting the pump’s output flow rate, pressure, and efficiency, and significantly influencing its working stability and reliability under different operating conditions. This paper reviews the structural principles, advantages, and disadvantages of current mainstream valve distribution, disc distribution, and shaft distribution methods, and discusses the main challenges they face in various applications. It focuses on analyzing how to improve piston pump performance by optimizing structural parameters, control strategies, and flow channel design. Furthermore, this paper introduces new flow distribution structures such as piston distribution and cylinder block distribution. The above provides a theoretical basis for the selection and innovation of flow distribution structures for piston pumps under different operating conditions in the future.
Birendra Kishore Roy
Halal Tourism and Artificial Intelligence · 2026
In the past few years, hospitality and tourism sector has experienced many significant changes by adapting to digital transformation. Many international hotels are using artificial intelligence in the form of robot chefs, which are specialized in performing a wide range of tasks by giving certain commands. Providing authentic service by maintaining service quality within a specific time remains a challenge for service providers in this industry. In a niche tourism sector where food is the priority for a specific group who are passionate about experiencing only Halal foods, for the mass production houses, it remains a challenge to provide standard products only by maintaining manual support. This chapter explains how artificial intelligence (AI) and automation are helping Halal tourism to improve service excellence. It elaborates on how advanced technology is supporting the operational needs of Muslim travelers, as they mainly seek Halal cuisine at a specific destination. What upgraded technical advancements, like voice recognition systems or the auto bots and service robots, or AI-driven mechanisms, have the possibilities in delivering service excellence in Halal tourism are also discussed in this chapter. This chapter explains that Halal tourism enterprises have a possible outcome if they adapt new technology to fulfill the needs of major communities. The research will help tourism policymakers and stakeholders obtain new strategies for greater success.
David S. Filippi, Bill Tom Tomlinson, Andrew W. Torrance
FIU Law Review · 2026
Law fundamentally exists to enable human cooperation, providing frameworks for everything from basic contracts to complex international agreements. As artificial intelligence systems grow more sophisticated, they may enable new ways that collaborative activity can occur. We posit the possibility of a new kind of AI entity: the “Apex Collaborator,” a computational system with capabilities for cooperation and partnership that are superior, in at least some ways, to those of humans. Just as apex predators shape the ecosystems in which they live through predation, Apex Collaborators would shape human-AI networks through their ability to enhance peaceful coexistence, collective problem-solving, and shared decision-making. “AI as an Apex Collaborator” flips the normal scripts of “AI as danger” or “AI as passive deliverer of benefits to humans,” instead conceiving of AI as a catalyst and enabler capable of lifting human abilities to cooperate above their evolutionary trajectory. This Article maps the legal architecture needed to guide AI development toward this collaborative potential, while simultaneously mapping fundamental implications particular coding decisions may have for the law. We address key areas requiring reform: liability regimes governing potential harms to humans, property, or other AIs, copyright law to enable AI training, structures and strictures for AI self-determination, clear accountability for AI-assisted actions and AI agents, interoperability standards, and alignment requirements. The Article proposes specific proactive and enforcement mechanisms for AI-ogenic conflict resolution, military restrictions, and data protection including crossborder transfer controls. We outline pathways to foster beneficial collaboration while preventing harmful applications. In particular, we explore the potential for AIs acting as Apex Collaborators to support humanity’s transition to sustainability. Our framework recognizes that as AI systems advance toward apex collaboration capabilities, they may need to participate in their own governance, monitoring and responding to not only harmful AI developments but also previously impossible benefits to humanity.
Nataliya Onishchenko, Dmytro Zhuravlov
Metaverse Science, Society and Law · 2026
The article examines the specific features of legal regulation of the artificial intelligence domain within the framework of the contemporary information society. The study argues for a human-centered approach to the development of legal mechanisms for governing AI, which must remain consistent with general legal principles and the protection of human rights and freedoms. Special attention is paid to the issues of legal provision and legal intervention in the AI sphere, along with the potential risks associated with the emergence of a digital dictatorship. The conclusion emphasizes the necessity of further academic and normative efforts aimed at regulating the AI domain.
Tran Thanh Du, Nguyen Huu The, Đặng Thị Bảo Dung et al.
Arab World English Journal · 2026
This study examines the difficulties encountered by English-majored master’s students at Thu Dau Mot University, Vietnam, including both first- and second-year cohorts, in learning a second foreign language, and proposes pedagogical strategies to improve their learning outcomes. A mixed-methods approach was employed, combining questionnaires (N = 30) and semi-structured interviews to obtain quantitative and qualitative insights. The questionnaire consisted of 44 items in two parts: (1) difficulties and influencing factors, and (2) learning strategies and coping mechanisms. Semi-structured interviews with ten randomly selected students included five open-ended questions focusing on factors affecting second foreign language learning, significant difficulties, learning habits, institutional support, and suggestions for improvement. The findings reveal that students encounter linguistic challenges such as interference from English, complex grammar, and limited exposure, as well as internal barriers including low motivation, test anxiety, and lack of confidence, alongside external constraints such as rigid schedules and limited practice opportunities To address these issues, students recommended self-regulated and collaborative learning, greater use of digital resources, interactive teaching methods, and stronger institutional support through flexible scheduling and curriculum adjustments. Integrating empirical data with theories of multilingualism and motivation, this study provides a comprehensive understanding of second-foreign language learning at Thu Dau Mot University. Students experience grammatical, lexical, and psychological barriers intensified by time pressure and institutional constraints. Collaborative practice, technology use, and real-life communication can help mitigate these challenges. The study concludes that multilingual competence is both cognitive and socially constructed, requiring alignment between learner needs, pedagogy, and Vietnam’s multilingual education goals.
Gina E Miranda Samuels
Disrupt the “Not-Telling” · 2026
Abstract This chapter explores several interlocking concepts core to my experience pursuing tenure and promotion to full professor as a Black woman of mixed-race heritage. Drawing on theories of epistemic injustice and critical feminist scholars, including bell hooks, Cherríe Moraga, and Gloria Anzaldúa, I articulate processes of mobilizing critical placemaking to counteract white supremacy culture, including toxic white femininity. The chapter first identifies dimensions of white supremacy culture within academia and then names targets of change necessary for valuing Black women. Highlighted as lessons, I focus on three mechanisms key to my praxis of surthrival: (1) detriangulating from dysfunctional patterns of toxic belonging, (2) navigating epistemic injustice and epistemicide within white supremacy culture, and (3) critical placemaking through cultivating healthy work habits, boundaries, and relationships. I hope this chapter is instructive in supporting these and other forms of critical praxis that honor us, and our knowledge work.
Yun-Peng Guo, Quan Wen, Yu-Yang Wang et al.
World Journal of Transplantation · 2026
Post-kidney transplant rejection is a critical factor influencing transplant success rates and the survival of transplanted organs. With the rapid advancement of artificial intelligence technologies, machine learning (ML) has emerged as a powerful data analysis tool, widely applied in the prediction, diagnosis, and mechanistic study of kidney transplant rejection. This mini-review systematically summarizes the recent applications of ML techniques in post-kidney transplant rejection, covering areas such as the construction of predictive models, identification of biomarkers, analysis of pathological images, assessment of immune cell infiltration, and formulation of personalized treatment strategies. By integrating multi-omics data and clinical information, ML has significantly enhanced the accuracy of early rejection diagnosis and the capability for prognostic evaluation, driving the development of precision medicine in the field of kidney transplantation. Furthermore, this article discusses the challenges faced in existing research and potential future directions, providing a theoretical basis and technical references for related studies.
Jeffrey Lidz, Tyler Knowlton
A Course in First Language Acquisition · 2026
Abstract This chapter discusses three examples of evolution equipping animals with problem-specific information to guide their learning (by shaping the space of hypotheses that they are willing to consider). Case one is rats being able to learn that sweet water, but not flashing lights and loud noises, can make them sick. Case two is bees using odor as a signal for food quality and color as a signal for food location. Case three is bees being able to learn the solar ephemeris—the path of the sun across the sky—even if they have been raised only ever seeing the sun in a single portion of the sky. Given these cases of learning mechanisms that are highly tailored to specific kinds of learning elsewhere in the animal kingdom, it’s suggested that the same might be true of humans. Namely, humans come to the language-learning table with specifically linguistic expectations.
Jeffrey Lidz, Tyler Knowlton
A Course in First Language Acquisition · 2026
Abstract This chapter focuses on the development of children’s ability to parse sentences in real time, and its effects on language learning. Properties of adult sentence processing—being incremental, predictive, and statistical—are discussed alongside experiments demonstrating that child parsing is characterized by the same properties. An additional property of child sentence processing is also introduced: It is ballistic, in the sense that children show difficulty revising parsing decisions once they have been made. The consequences of the child’s developing parser for language acquisition are then considered. In particular, sometimes what the child perceives to be the grammatical structure of a sentence may not be the intended grammatical structure. Consequences for theorists are also discussed, namely that it is important to understand not just the input (the data available) but also the intake to the learning mechanism (the input as it is processed by children).
Leena Sanjay Pelne
International Journal for Research in Applied Science and Engineering Technology · 2026
Phishing attacks remain one of the most critical cybersecurity threats, exploiting users through fraudulent websites to obtain sensitive information such as credentials and financial data. Traditional rule-based detection systems often lack adaptability to evolving attack strategies. This study proposes an AI-driven cybersecurity framework for phishing website detection using supervised machine learning models. The UCI Phishing Website Dataset consisting of 1353 instances and 10 security-related attributes was used for experimentation. Three classifiers—Logistic Regression, Support Vector Machine (SVM), and Random Forest—were implemented and comparatively evaluated. Hyperparameter optimization using GridSearchCV with 5-fold cross-validation was performed to enhance predictive performance. The dataset was split into 70% training and 30% testing subsets. Performance evaluation was conducted using accuracy, precision, recall, F1-score, and confusion matrix analysis. Experimental results show that the optimized Random Forest model achieved approximately 91% accuracy, outperforming Logistic Regression and SVM models. Feature importance analysis highlights that attributes such as SFH and SSLfinal_State significantly influence classification outcomes. The findings demonstrate that ensemble-based AI techniques strengthen phishing detection systems and provide scalable, intelligent cybersecurity defense mechanisms.
Marjolein Muller, Mark F.C. van Leeuwen, C.F.E. Hoffmann et al.
Brain stimulation · 2025
Programming deep brain stimulation (DBS) of the subthalamic nucleus for optimal symptom control in Parkinson's Disease (PD) requires time and trained personnel. Novel implantable neurostimulators allow local field potentials (LFP) recording, which could be used to identify the optimal (chronic) stimulation contact. However, literature is inconclusive on which LFP features and prediction techniques are most effective.
Nandini Sengupta, Rezaul Begg, Aravinda S. Rao et al.
PloS one · 2025
Biofeedback-based treadmill training generally involves 10 or more sessions to assess its effectiveness during stroke rehabilitation. Improvements are seen in some patients during the assessment, while others do not progress. Our aim in this study is to determine (i) if signs of progress are evident from the initial training session and (ii) whether quantitative measurements between consecutive training sessions can guide interventions for non-progressing patients. The study analyzes Minimum Foot Clearance (MFC) data from 15 stroke patients during their baseline and second training sessions to predict outcomes in the post-assessment phase. Based on early biofeedback training data, we propose a novel approach using cosine similarity (CS), correlation coefficient (CC) and cross-correlation distance (XCRD) measures to predict post-assessment improvements in stroke patients. We also introduce a new real-time adherence assessment metric (RAAM) metric to quantify improvements in adherence to feedback between consecutive training sessions, enabling more targeted interventions. The proposed approach using CS, CC and XCRD adherence indicators demonstrates 100% accuracy in predicting improvement during post-assessments. The results show that patients with MFC values dissimilar to their baseline while adhering to targeted feedback are more likely to improve. The work also indicates that patients who don't show significant overall improvement may benefit from extended training periods, suggesting the potential for personalized rehabilitation strategies.
Rahul Meena, Saumyaranjan Sahoo, Anubhav Mishra
Journal of Hospitality and Tourism Technology · 2026
Purpose Through the integration of the heuristic-systematic model (HSM) and the trust-building model, this study aims to investigate the intricate mechanisms through which online travel chatbots’ (OTCs) characteristics influence trust formation. This study specifically examines how systematic and heuristic processing routes distinctly shape cognitive and affective trust development in human–AI (artificial intelligence) interactions, addressing fundamental gaps in digital trust literature. Design/methodology/approach The investigation uses a systematic research design that systematically examines user interactions with OTC platforms. Three hundred participants engaged in structured travel planning scenarios, providing a robust data set for analyzing trust formation patterns. The methodology incorporates both controlled exposure and naturalistic interaction elements, enabling systematic examination of systematic processing (via communication quality and trendiness assessment) and heuristic processing (through anthropomorphic features and interaction enjoyment). Structural equation modeling techniques, coupled with serial mediation analysis, were used to test the theoretical framework. Findings The results reveal that OTC characteristics directly influence both affective and cognitive trust components. Communication quality and trendiness significantly impact cognitive trust, while anthropomorphism and interactional enjoyment influence affective trust. Both trust components strongly affect reuse intention, but their impact on word-of-mouth varies, with cognitive trust showing stronger direct effects. Research limitations/implications This study establishes a comprehensive framework for understanding trust formation in AI-powered travel services, offering a foundation for future research in human–agent trust dynamics. Practical implications The findings enable OTC developers to optimize trust-building features and help travel companies enhance user adoption through targeted implementation of trust-inducing characteristics. Originality/value This research advances theoretical understanding in three distinctive ways. First, it extends HSM application in digital environments by delineating how systematic and heuristic processing manifest uniquely in OTC interactions. Second, it enriches trust-building theory by revealing the distinct mediating roles of cognitive and affective trust in human–AI exchanges. Third, it establishes a comprehensive framework for understanding trust formation in AI-powered services, particularly in high-involvement decision contexts such as travel planning. These insights reveal patterns of trust development specific to human–AI interactions that diverge from traditional human–human trust formation mechanisms documented in earlier studies.