Despite the growing capabilities of large language models, concerns exist about the biases they develop. In this paper, we propose a novel, automated mechanism for debiasing through specified dataset augmentation in the lens of bias producers that can be useful in a variety of industries, especially ones that are “restricted” and have limited data. We consider that bias can occur due to intrinsic model architecture and dataset quality. The two aspects are evaluated using two different metrics we created. We show that our dataset augmentation algorithm reduces bias as measured by our metrics. Our code can be found on an online GitHub repository. Full article

Background: This report focuses on the treatment histories of 21 patients diagnosed with Dravet syndrome (DRVT) under the care of the Mother and Child Institute in Warsaw. This paper aims to present typical treatment schemes for patients with drug-resistant epilepsy, as well as to highlight the influence of genetic diagnosis on pharmacotherapeutic management and to present an economic analysis of hospitalization costs. This paper will also summarize the effectiveness of the latest drugs used in DRVT. Methods: Clinical data were collected retrospectively from available medical records. The effectiveness of anticonvulsant treatment was assessed based on epileptic seizure diaries and observations by caregivers and pediatric neurologists. Results: The study group (n = 21) consisted of patients aged 3–26 years. Orphan drugs dedicated to Dravet syndrome were introduced in all patients due to the genetic diagnosis, which significantly improved the patients’ clinical conditions. The breakthrough drugs were stiripentol (in 16/21) and fenfluramine (in 3/21). Conclusions: In recent years, molecular genetics has rapidly developed in Poland, along with a steady increase in knowledge of Dravet syndrome among the medical profession. Early and precise diagnosis provides the opportunity to target treatment with drugs dedicated to Dravet syndrome with high efficacy. Full article

Milk and dairy products are included in the list of the Food Security Doctrine and are of paramount importance in the diet of the human population. At the same time, the presence of many macro- and microcomponents in milk, as available sources of carbon and energy, as well as the high activity of water, cause the rapid development of native and pathogen microorganisms in it. The goal of the work was to assess the possibility of using an array of gas chemical sensors based on piezoquartz microbalances with polycomposite coatings to assess the microbiological indicators of milk quality and to compare the microflora of milk samples. Piezosensors with polycomposite coatings with high sensitivity to volatile compounds were obtained. The gas phase of raw milk was analyzed using the sensors; in parallel, the physicochemical and microbiological parameters were determined for these samples, and species identification of the microorganisms was carried out for the isolated microorganisms in milk. The most informative output data of the sensor array for the assessment of microbiological indicators were established. Regression models were constructed to predict the quantity of microorganisms in milk samples based on the informative sensors’ data with an error of no more than 17%. The limit of determination of QMAFAnM in milk was 243 ± 174 CFU/cm³. Ways to improve the accuracy and specificity of the determination of microorganisms in milk samples were proposed. Full article

Insects are the most diverse form of life, and as such, they interact closely with humans, impacting our health, economy, and agriculture. Beneficial insect species contribute to pollination, biological control of pests, decomposition, and nutrient cycling. Pest species can cause damage to agricultural crops and vector diseases to humans and livestock. Insects are often exposed to toxic xenobiotics in the environment, both naturally occurring toxins like plant secondary metabolites and synthetic chemicals like herbicides, fungicides, and insecticides. Because of this, insects have evolved several mechanisms of resistance to toxic xenobiotics, including sequestration, behavioral avoidance, and enzymatic degradation, and in many cases had developed symbiotic relationships with microbes that can aid in this detoxification. As research progresses, the important roles of these microbes in insect health and function have become more apparent. Bacterial symbionts that degrade plant phytotoxins allow host insects to feed on otherwise chemically defended plants. They can also confer pesticide resistance to their hosts, especially in frequently treated agricultural fields. It is important to study these interactions between insects and the toxic chemicals they are exposed to in order to further the understanding of pest insect resistance and to mitigate the negative effect of pesticides on nontarget insect species like Hymenopteran pollinators. Full article

Background: The use of standardized patient simulation in psychiatric nursing education addresses the unique challenges presented by mental healthcare settings. Students’ attitudes toward clinical simulation remain predominantly favorable, with many expressing enthusiasm for the opportunities it provides in terms of embracing challenges, enhancing realism, and promoting critical thinking through problem solving, decision-making, and adaptability. Methods: This quantitative study used a cross-sectional, descriptive, correlation design to investigate the effectiveness of standardized patient simulation as a teaching method in the Psychiatric and Mental Health nursing course in a university setting. A total of 84 nursing students were recruited for the convenience sample. Data were collected using a three-part questionnaire survey which included the following: a demographic data sheet, the Student Satisfaction and Self-confidence in Learning Scale, and a narrative open-ended question asking the participants to write the advantages and disadvantages of their simulation experience. Data were analyzed using the statistical software JMP pro17. Results: The total satisfaction with learning subscale score ranged between 5 and 25 with a mean score of 19.36 ± 6.32. The total self-confidence subscale score ranged between 8 and 40 with a mean score of 30.87 ± 9.1. Pearson’s correlation coefficient r revealed a statistically significant positive relationship between the participants’ satisfaction with the learning experience and their self-confidence (t = 0.923, p < 0.0001). Approximately 91.7% of the students recommended using simulation. The results confirmed the students’ recommendations of simulation use in teaching psychiatric and mental health courses; furthermore, the results showed a statistically significant positive correlation with the total SSLS (p = 0.01) and satisfaction with learning subscale (0.003). Participants reported that authentic, practical, comfortable, and safe learning environments contributed to an enriched learning experience. Additionally, factors such as timesaving, access to information, cost-effectiveness, standardized teaching, varied exposure, skill development, and immediate feedback also enhanced the learning experience through patient simulation in psychiatric and mental health nursing. Conclusion: Simulations can contribute efficiently and positively to psychiatric and mental health nursing education in a manner that optimizes the learning experience while ensuring the consistency of student learning in a safe learning environment. Full article

Oral anticoagulant therapy (OAT) for managing atrial fibrillation (AF) encompasses vitamin K antagonists (VKAs, such as warfarin), which was the mainstay of anticoagulation therapy before 2010, and direct-acting oral anticoagulants (DOACs, namely dabigatran etexilate, rivaroxaban, apixaban, edoxaban), approved for the prevention of AF stroke over the last thirteen years. Due to the lower risk of major bleeding associated with DOACs, anticoagulant switching is a common practice in AF patients. Nevertheless, there are issues related to OAT switching that still need to be fully understood, especially for patients in whom AF and heart failure (HF) coexist. Herein, the effective impact of the therapeutic switching from warfarin to DOACs in HF patients with AF, in terms of cardiac remodeling, clinical status, endothelial function and inflammatory biomarkers, was assessed by a machine learning (ML) analysis of a clinical database, which ultimately shed light on the real positive and pleiotropic effects mediated by DOACs in addition to their anticoagulant activity. Full article

Infections caused by KPC-producing K. pneumoniae continue to pose a significant clinical challenge due to their emerging resistance to new antimicrobials. We investigated the association between two drugs whose roles have been repurposed against multidrug-resistant bacteria: fosfomycin and temocillin. Temocillin exhibits unusual stability against KPC enzymes, while fosfomycin acts as a potent “synergizer”. We conducted in vitro antimicrobial activity studies on 100 clinical isolates of KPC-producing K. pneumoniae using a combination of fosfomycin and temocillin. The results demonstrated synergistic activity in 91% of the isolates. Subsequently, we assessed the effect on Galleria mellonella larvae using five genetically different KPC-Kp isolates. The addition of fosfomycin to temocillin increased larvae survival from 73 to 97% (+Δ 32%; isolate 1), from 93 to 100% (+Δ 7%; isolate 2), from 63 to 86% (+Δ 36%; isolate 3), from 63 to 90% (+Δ 42%; isolate 4), and from 93 to 97% (+Δ 4%; isolate 10). Among the temocillin-resistant KPC-producing K. pneumoniae isolates (24 isolates), the addition of fosfomycin reduced temocillin MIC values below the resistance breakpoint in all isolates except one. Temocillin combined with fosfomycin emerges as a promising combination against KPC-producing K. pneumoniae, warranting further clinical evaluation. Full article

Text classification is not only a prerequisite for natural language processing work, such as sentiment analysis and natural language reasoning, but is also of great significance for screening massive amounts of information in daily life. However, the performance of classification algorithms is always affected due to the diversity of language expressions, inaccurate semantic information, colloquial information, and many other problems. We identify three clues in this study, namely, core relevance information, semantic location associations, and the mining characteristics of deep and shallow networks for different information, to cope with these challenges. Two key insights about the text are revealed based on these three clues: key information relationship and word group inline relationship. We propose a novel attention feature fusion network, Attention Pyramid Transformer (APTrans), which is capable of learning the core semantic and location information from sentences using the above-mentioned two key insights. Specially, a hierarchical feature fusion module, Feature Fusion Connection (FFCon), is proposed to merge the semantic features of higher layers with positional features of lower layers. Thereafter, a Transformer-based XLNet network is used as the backbone to initially extract the long dependencies from statements. Comprehensive experiments show that APTrans can achieve leading results on the THUCNews Chinese dataset, AG News, and TREC-QA English dataset, outperforming most excellent pre-trained models. Furthermore, extended experiments are carried out on a self-built Chinese dataset theme analysis of teachers’ classroom corpus. We also provide visualization work, further proving that APTrans has good potential in text classification work. Full article

The current research paper presents the results of the first regional assessment of sediment contamination by dioxins (polychlorinated dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs) and dioxin-like polychlorinated biphenyls (dl-PCBs)) in the south-eastern part of the Baltic Sea (Lithuanian and Polish marine areas) during the periods of 2014 and 2019–2020. In total, 143 surface and core sediment samples were taken of existing offshore dredged-soil-disposal sites in the area of the former shipyard in the Port of Gdynia (Poland), as well as in a profile from the nearshore to the deeps of the Gdansk basin, following the natural pattern of sediment migration in the region. The obtained results indicated wide variation in both the total content of the investigated compounds as well as the profiles of congeners, indicating the likely sources of their origin. Based on the obtained concentration characteristic profiles of the congeners, we determined the amount of dioxins and the likely sources of their origin in the Gdansk Basin. The profiles showed elevated contents of octa- and hepta-chlorodibenzodioxins (OCDD and HpCDD) in the sediments, while the fractions of most other toxic congeners were considerably lower. The domination of OCDF in the spectrum of the studied PCDFs suggests the possible contribution of industrial processes. The obtained results have filled the gaps in our knowledge while providing a perfect background for more detailed discussions concerning the accumulation of dioxins in surface sediments from the south-eastern part of the Baltic Sea. Full article

Based on panel data from 285 prefecture-level cities in China from 2019 to 2023, the synergistic effects of the digital economy, green technology innovation, and ecological environment quality were analyzed. First, using the entropy method, the measurement dimensions of the indicators of the digital economy, green technology innovation, and ecological environment quality were obtained. Second, employing a neural network model with these measurements as input variables, the interactive relationship among the digital economy, green technology innovation, and ecological environment quality was explored. Finally, based on the calculation results of the neural network model, the importance and impact of each input parameter on ecological environment quality were determined using weight analysis methods. The research findings indicate: (1) Utilizing the entropy method, the measurement dimensions of the indicators of the digital economy, green technology innovation, and ecological environment quality were obtained. Analysis of each indicator measurement reveals that environmental pressure has a significant impact on ecological environment quality, with significant differences in environmental pressure among different regions. Industrial digitization emerges as the core factor influencing the digital economy, being the most significant driving effect, followed by digital industrialization. Green technology innovation is crucial for promoting environmental protection and achieving high-quality green economic development. (2) Based on the neural network model, the interactive relationship among the digital economy, green technology innovation, and ecological environment quality was revealed. The results indicate that the digital economy has a direct impact on improving ecological environment quality. The relationship between the digital economy and the ecological environment exhibits nonlinear effects, with the rate of change in environmental pressure and environmental status measurements initially increasing significantly and then gradually slowing down as the measurement levels of digital industrialization and industrial digitization increase. Improvement in digital governance and data value measurement levels will contribute to enhancing environmental status and environmental governance levels. (3) Through weight analysis, it was found that in terms of direct effects, industrial digitization, and digital industrialization have the most significant impact on environmental pressure, with importance coefficients of 0.45 and 0.3, respectively, while data valorization has the least impact. Regarding intermediary effects, industrial digitization and green technology innovation have the most significant impact on environmental pressure, while digital governance and green technology innovation have a relatively clear impact on environmental status and environmental governance. These results lay the foundation for promoting the coordinated cooperation between the digital economy and green technology innovation and for advancing the establishment of a win–win situation between economic development and environmental protection. Full article

Yeokwisan (YWS) is an herbal medicine prescription consisting of six oriental herbal medicines, developed to treat reflux esophagitis. We focused on developing an analytical method capable of simultaneously quantifying 13 compounds in YWS samples using high-performance liquid chromatography–photodiode array detection (HPLC–PDA) and ultra-performance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS) and exploring their antioxidant effects. All compounds examined in both analytical systems were chromatographically separated on a SunFire^TM C₁₈ (4.6 × 250 mm, 5 μm) column and an Acquity UPLC BEH C₁₈ (2.1 × 100 mm, 1.7 μm) column using gradient elution of a water–acetonitrile mobile phase. Antioxidant effects were evaluated based on radical scavenging activity (DPPH and ABTS tests) and ferrous ion chelating activity. In two analytical methods, the coefficient of determination of the regression equation was ≥0.9965, the recovery range was 81.11–108.21% (relative standard deviation (RSD) ≤ 9.33%), and the precision was RSD ≤ 11.10%. Application of the optimized analysis conditions gave quantitative analysis results for YWS samples of 0.02–100.36 mg/g. Evaluation of the antioxidant effects revealed that baicalein and baicalin exhibit significant antioxidant activity, suggesting that they play an important role in the antioxidant effects of YWS. Full article

Two years after the COVID-19 pandemic, the Greek economy seems to have overcome the turmoil of the pandemic crisis as well as that of the following energy crisis. Nevertheless, it would be wrong to assume that the Greek economy has returned to a sound state, since this was not really the case even before the pandemic. Furthermore, the anemic growth rates of the pre-pandemic period were followed by an equally weak average growth rate (including the impact of the pandemic), as some of the significant fundamental weaknesses of the Greek economy, which had accumulated over time and constituted the real origin of the Greek crisis, have not been properly addressed yet. This paper attempts a complete mapping of the current state of the Greek economy, offering an insight into the external and internal determinants affecting it. Full article

Ultrasounds are considered an emerging technology in the wine industry. Concretely, in 2019, the International Organization of Vine and Wine (OIV) officially approved their use for the treatment of crushed grapes to increase the level of phenolic compound extraction. The main objective of this study was to validate an untargeted metabolomics approach as an analytical tool for identifying novel markers associated with sonication. To do so, the influence of a sonication treatment on the metabolic profile was studied in four typically commercial varietal wines, i.e., two red wines from ‘Syrah’ and ‘Cabernet Sauvignon’ grapes and two white wines from ‘Macabeo’ and ‘Airén’ grapes. A robust classification and prediction model was created employing supervised techniques such as partial least-squares discriminant analysis (PLS-DA). The findings indicated that the grapes subjected to high-power ultrasound conditions experienced cell wall disruption due to the cavitation phenomenon, resulting in significant changes in various phenolic compounds (including hydroxycinnamic acids and flavonoids) present in these wines compared to wines from non-sonicated grapes. Additionally, new metabolites were tentatively identified through untargeted metabolomics techniques. This study represents the successful application of the untargeted metabolomics approach employing a UHPLC-QTOF system to discern how grape sonication affects bioactive secondary metabolites in wines. Full article

To date, BIM has been primarily utilized in cost and schedule management, an interference check between architectural and structural models and systems based on geometric data in the process of the construction life cycle. However, there is a lack of research that utilizes the information contained in the BIM model to review whether the proposed architectural model is appropriately designed in accordance with each country’s building regulations or building codes or that proposes a model optimized for laws and standards. ‘Building code checking’ is the step of reviewing whether a building designed based on the building codes is suitable for being constructed as a building. However, this process consumes significant time and money and leads to human errors due to the manual review process. This study included implementation of an algorithm based on the Korean building code. In this study, there was the process of selection of codes when architects interpret building codes in common and implementation based on the codes selected. Next, modeling based on DXF files from NGII (National Geographic Information Institute) was applied to the algorithm developed in this study. Last, it includes case studies that compare the outputs of the algorithm with the real buildings, which had received real code checking, to make sure the algorithm in this paper is working properly. The implementation of such an automated system has the potential to significantly improve the efficiency and effectiveness of the building design and construction process. It can help architects to quickly and accurately identify potential legal issues and provide alternative solutions that meet regulatory requirements. This, in turn, can lead to reduced project costs, improved quality of designs, and faster project delivery times. Full article

Radon, a radioactive inert gas that comes from the decay of naturally occurring radioactive species, poses a substantial health risk due to its involvement in lung cancer carcinogenesis. This work proposes a metrological approach for determining radon exhalation rates from diverse building materials. This methodology employs an electrostatic collection chamber for alpha spectrometry of radon isotopic decay products. Experimental evaluations were conducted particularly focusing on volcanic gray tuff from Sant’Agata de’ Goti (Campania region, Italy), a material commonly utilized in construction, to assess radon exhalation rates. The study aligns with Legislative Decree 101/2020, a transposition of European Directive 59/2013/Euratom, highlighting the need to identify materials with a high risk of radon exhalation. Moreover, this work supports the goals of the Italian National Radon Action Plan related to the aforementioned decree, aiming to develop methodologies for estimating radon exhalation rates from building materials and improving radioprotection practices. Full article

In this paper, we define a cohomology theory of a modified $\lambda$-differential left-symmetric algebra. Moreover, we introduce the notion of modified $\lambda$-differential left-symmetric 2-algebras, which is the categorization of a modified $\lambda$-differential left-symmetric algebra. As applications of cohomology, we classify linear deformations and abelian extensions of modified $\lambda$-differential left-symmetric algebras using the second cohomology group and classify skeletal modified $\lambda$-differential left-symmetric 2-algebra using the third cohomology group. Finally, we show that strict modified $\lambda$-differential left-symmetric 2-algebras are equivalent to crossed modules of modified $\lambda$-differential left-symmetric algebras. Full article

Image detection technology is of paramount importance across various fields. This significance is not only seen in general images with everyday scenes but also holds substantial research value in the field of remote sensing. Remote sensing images involve capturing images from aircraft or satellites. These images typically feature diverse scenes, large image formats, and varying imaging heights, thus leading to numerous small-sized targets in the captured images. Accurately identifying these small targets, which may occupy only a few pixels, is a challenging and active research area. Current methods mainly fall into two categories: enhancing small target features by improving resolution and increasing the number of small targets to bolster training datasets. However, these approaches often fail to address the core distinguishing features of small targets in the original images, thus resulting in suboptimal performance in fine-grained classification tasks. To address this situation, we propose a new network structure DDU (Downsample Difference Upsample), which is based on differential and resolution changing methods in the Neck layer of deep learning networks to enhance the recognition features of small targets, thus further improving the feature richness of recognition and effectively solving the problem of low accuracy in small target object recognition. At the same time, in order to take into account the recognition effect of targets of other sizes in the image, a new attention mechanism called PNOC (protecting the number of channels) is proposed, which integrates small target features and universal object features without losing the number of channels, thereby increasing the accuracy of recognition. And experimental verification was conducted on the PASCAL-VOC dataset. At the same time, it was applied to the testing of the fine-grained MAR20 dataset and found that the performance was better than other classic algorithms. At the same time, because the proposed framework belongs to a one-stage detection method, it has good engineering applicability and scalability, and universality in scientific research applications are good. Through comparative experiments, it was found that our algorithm improved the performance of the mAP by 0.7% compared to the original YOLOv8 algorithm. Full article

New and innovative technologies have expanded the quality and applications of aluminum welding in the maritime, aerospace, and automotive industries. One such technology is the addition of nanoparticles to aluminum matrices, resulting in improved strength, operating temperature, and stiffness. Furthermore, researchers continue to assess pertinent factors that improve the microstructure and mechanical characteristics of aluminum welding by enabling the optimization of the manufacturing process. Hence, this research explores alternatives, namely cost-effective aluminum welding fillers reinforced with niobium diboride nanoparticles. The goal has been to improve weld quality by employing multi-objective optimization, attained through a central composite design with a response surface model. The model considered three factors: the amount (weight percent) of nanoparticles, melt stirring speed, and melt stirring time. Filler hardness and porosity percentage served as response variables. The optimal parameters for manufacturing this novel filler for the processing conditions studied are 2% nanoparticles present in a melt stirred at 750 rpm for 35.2 s. The resulting filler possessed a 687.4 MPA Brinell hardness and low porosity, i.e., 3.9%. Overall, the results prove that the proposed experimental design successfully identified the optimal processing factors for manufacturing novel nanoparticle-reinforced fillers with improved mechanical properties for potential innovative applications across diverse industries. Full article

The construction industry is a challenging field for the application of robots. In particular, bridge construction, which involves many tasks at great heights, makes it difficult to implement robots. To construct a bridge, it is necessary to build numerous piers that can support the bridge deck. Pier construction involves a series of tasks including rebar connection, formwork installation, concrete pouring, formwork dismantling, and formwork reinstallation. These activities require working at heights, presenting a significant risk of falls. If bridge construction could be performed remotely using robots instead of relying on human labor, it would greatly contribute to the safety of bridge construction. This paper proposes a multi-robot system capable of remote operation and automation for rebar structure connection, concrete pouring, and concrete vibrating tasks in pier construction. The proposed multi-robot system for pier construction is composed of three robot systems. Each robot system consists of a robot arm mounted on a mobile robot that can move along rails. And to apply the proposed system to a construction site, it is essential to implement a compliance control algorithm that adapts to external forces. In this paper, we propose an admittance control that takes into account the weight of the tool for the compliance control of the proposed robot, which performs tasks by switching between various construction tools of different weights. Furthermore, we propose a synchronization control method for the multi-robot system to connect reinforcing structures. We validated the proposed algorithm through simulation. Furthermore, we developed a prototype of the proposed system to verify the feasibility of the suggested hardware design and control. Full article

Thermally stable high-performance phenolic resin aerogels (PRAs) are of great interest for thermal insulation because of their light weight, fire retardancy and low thermal conductivity. However, the drawbacks of PRA synthesis, such as long processing time, inherent brittleness and significant shrinkage during drying, greatly restrict their wide applications. In this work, PRAs were synthesized at ambient pressure through a near-net shape manufacturing technique, where boron-containing thermosetting phenolic resin (BPR) was introduced into the conventional linear phenolic resin (LPR) to improve the pore characteristics, mechanical properties and thermal performances. Compared with the traditional LPR-synthesized aerogel, the processing time and the linear shrinkage rate during the drying of the PRAs could be significantly reduced, which was attributed to the enhanced rigidity and the unique bimodal pore size distribution. Furthermore, no catastrophic failure and almost no mechanical degradation were observed on the PRAs, even with a compressive strain of up to 60% at temperatures ranging from 25 to 200 °C, indicating low brittleness and excellent thermo-mechanical stability. The PRAs also showed outstanding fire retardancy. On the other hand, the PRAs with a density of 0.194 g/cm³ possessed a high Young’s modulus of 12.85 MPa and a low thermal conductivity of 0.038 W/(m·K). Full article

UAV swarm passive positioning technology only requires the reception of electromagnetic signals to achieve the positioning and tracking of radiation sources. It avoids the active positioning strategy that requires active emission of signals and has the advantages of good concealment, long acting distance, and strong anti-interference ability, which has received more and more attention. In this paper, we propose a new UAV swarm formation flight method based on pure azimuth passive positioning. Specifically, we propose a two-circle positioning model, which describes the positional deviation of the receiving UAV using trigonometric functions relative to the target in polar coordinates. Furthermore, we design a two-step adjustment strategy that enables the receiving UAV to reach the target position efficiently. Based on the above design, we constructed an optimized UAV swarm formation scheme. In experiments with UAV numbers of 8 and 20, compared to the representative comparison strategy, the proposed UAV formation scheme reduces the total length of the UAV formation by 34.76% and 55.34%, respectively. It demonstrates the effectiveness of the proposed method in the application of assigning target positions to UAV swarms. Full article

Microbial fuel cells (MFCs) have garnered significant attention due to their capacity to generate electricity using renewable and carbon-neutral energy sources such as wastewater. Extensive experimental work and modeling techniques have been employed to dissect these processes and understand their respective impacts on electricity generation. The driving force is to enhance MFC performance for practical applications commercially. Among the various statistical modeling approaches, one particularly robust tool is the Design of Experiments (DoE). It serves to establish the relationships between different variables that influence MFC performance and allows for the optimization of the MFC configuration and operation for scaled-up performances in terms of bioelectricity generation. This study focused on optimizing microbial fuel cells (MFCs) for bioelectricity production using industrial wastewater treatment, employing the Box–Behnken design (BBD) methodology. Through an analysis of response surface models and ANOVA tests, it was found that a combined approach of reduced quadratic, reduced two-factor interaction, and linear models yielded sound results, particularly in voltage yield, COD removal, and current density. Second-order regression models predicted optimal conditions for various parameters, with surface area, temperature, and catholyte dosage identified as critical input variables for optimization. Under these conditions, conducted by the four-factor and three-level Box–Behnken design methodology in a double-chamber MFC unit considering eight output variables—CCV yield, % COD removal, current density, power density, % TSS removal, % CE, and % ${\mathrm{P}\mathrm{O}}_4^{3-}$—the optimum values were 700 mV, 54.4%, 54.4 mA/m², 73.7 mW/m², 99%, 21.2%, and 100%, respectively. At optimum operating conditions, the results revealed a desirability of 76.6% out of a total of 92 iterations. The paper highlights the effectiveness of statistical ANOVA fit-statistics modeling and optimization in enhancing DCMFC performance, recommending its use as a sustainable bioenergy source. Furthermore, validation results supported the above optimization output response findings and confirmed the viability of biorefinery wastewater as an anolyte for scaling up DCMFC bioelectricity generation. Full article