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.

Narcis Pous, Sebastià Puig, M. Dolors Balaguer et al.

Environmental Science: Water Research & Technology • 2017

This paper evaluates the influence of HRT and nitrate content on denitrifying BES.

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.

[object Object], [object Object]

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%.

[object Object], [object Object], [object Object] 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.

[object Object], [object Object], [object Object] 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.

[object Object], [object Object], [object Object] 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.

[object Object], [object Object], [object Object] 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.

[object Object], [object Object], [object Object] 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.

[object Object]

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.

[object Object], [object Object], [object Object]

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.

[object Object], [object Object]

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.

[object Object], [object Object], [object Object] 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.

[object Object]

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.

[object Object], [object Object], [object Object] 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.

[object Object], [object Object]

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.

[object Object], [object Object]

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).

[object Object]

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.

[object Object], [object Object], [object Object] 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.

[object Object], [object Object], [object Object] 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.

[object Object], [object Object], [object Object]

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.

[object Object], [object Object], [object Object] et al.

World Journal of Gastroenterology • 2026

BACKGROUND Esophageal pleural fistula (EPF) primarily arises as a complication of esophageal surgery, malignant tumors, or trauma. The high mortality rate associated with EPF underscores the critical need for early diagnosis and aggressive treatment, which often involves a multidisciplinary approach including thoracic drainage, broad-spectrum antibiotics, nutritional support, and often surgical or endoscopic intervention. Despite its clinical severity, a corresponding animal disease model for mechanistic and therapeutic research remains unavailable. AIM To establish a stable and reproducible EPF animal model using magnetic compression technology (MCT). METHODS EPF modeling surgery was successfully performed on 20 New Zealand white rabbits (weight: 2-3 kg) with our self-developed MCT device. Postoperatively, radiographic confirmation of magnet positioning was conducted within 24 hours. Fistula tract tissue samples were subjected to hematoxylin-eosin and Masson’s trichrome histochemical staining. Pathological specimens were intentionally withheld from a subset of rabbits (n = 8) to assess long-term stability; these animals were monitored for a prolonged period until postoperative day (POD) 30 before euthanasia, allowing for observation of chronic changes. RESULTS The rabbit model of EPF was successfully established. The average surgical time was 26.6 ± 4 minutes. Magnets were spontaneously excreted at 7.0 ± 0.7 days postoperatively (n = 18/20). Pleural abscesses developed in 14 rabbits (70%). All rabbits (n = 8) reached the 30-day endpoint without intervention. Data analysis revealed no significant correlation between the abscess size, surgery time, anesthesia time, magnet discharge time, and weight changes within POD 9. Gross pathology confirmed the formation of EPFs and pleural abscesses. Spontaneous healing tendencies were observed in a subset of fistulas (n = 6). Histological analysis revealed esophageal epithelial migration advancing toward the fistula lumen, whereas pleural abscess cavities contained extensive necrotic debris characterized by neutrophilic infiltration and fibrin deposition, collectively validating the model’s success. CONCLUSION The magnetic compression-derived rabbit EPF model exhibits high establishment success and prolonged viability, enabling robust pathophysiological research.

[object Object]

Metafizika Journal • 2026

The aim of this article is to investigate the shortcomings of classical linguistic theories in explaining phenomena such as word creation, meaning formation, and influence, and to propose a unified model based on the principles of quantum physics as an alternative. The research has revealed that while cognitive, generative, and usagebased theories are useful in explaining the mechanical and social aspects of the word, they cannot explain its enigmatic aspects such as creativity, meaning collapse (decoherence), and non-local influence. The proposed "Quantum Paradigm of the Word" model interprets the word as a quantum information packet with meaning encoded at a fundamental level. This model consists of four main stages: (1) Potential in the Quantum Information Field (the probability cloud of an abstract idea), (2) Creative Collapse and Encoding (Fuzuli's moment of "creation from non-existence"), (3) Energy Transmission (the physical carrier as sound), and (4) Reception and Re-Collapse (meaning formation in the listener). The research determined that quantum principles (superposition, collapse, entanglement) show remarkable parallels with the phenomenon of the word. The model explains the physiological effect of words (blessing/curse) as the energetic intervention of the information packet into a biological system, and the impact of artistry as a coherent flow of information. Cases of non-local influence, such as "speaking behind one's back," can be explained within the framework of semantic entanglement or social network effects. In conclusion, the quantum paradigm expands our understanding of the ontology of the word, presenting it not merely as a social convention but also as a fundamental phenomenon operating in accordance with the basic energy-information principles of the universe. The model creates a new platform for dialogue between scientific thought and metaphysical concepts and offers prospects for future transdisciplinary research.

[object Object]

Psikiyatride Güncel Yaklaşımlar • 2026

Psychodrama is an experiential psychotherapeutic method whose effectiveness has been validated through extensive qualitative and quantitative research, finding broad applicability in both clinical and sub-clinical contexts. Contemporary theorists and authors agree that action and spontaneity-creativity, the two conceptual foundations of psychodrama, constitute the therapeutic core underlying its effectiveness. However, the existing literature lacks a comprehensive explanation of the neural mechanisms through which action, spontaneity, and creativity exert their therapeutic effects. The recent and growing dialogue between neuroscience and psychotherapy is likely to shed light on the neural mechanisms underlying psychodrama. On the other hand, the action-oriented nature of psychodrama, which distinguishes it from other psychotherapy approaches, necessitates examining it through the lens of another discipline—ecological psychology. This article proposes an integrated neuro-psychodramatic model to elucidate the neuroscientific and psychological mechanisms underpinning spontaneity and creativity in psychodrama, highlighting how the ecological psychology concept of affordances, situated within the perception-action cycle framework, mediates psychodramatic spontaneity-creativity, and demonstrating how the neural bases of cognitive/behavioral flexibility parallel those underlying spontaneity-creativity. This integrative model enhances theoretical understanding, guides future empirical research, and aims to optimize psychodramatic therapeutic interventions within contemporary neuroscientific and psychological paradigms.

[object Object], [object Object], [object Object] et al.

American journal of epidemiology • 2025

Living in racially and ethnically segregated neighborhoods may increase the risk of breast cancer. We examined associations between neighborhood racial and ethnic composition typology and risk of primary invasive breast cancer in a population-based sample of 102 615 African American/Black, Japanese American, Native Hawaiian, Latino, and White females residing in California and Hawaii from the Multiethnic Cohort (MEC) Study from 1993 through 2019. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using multivariable Cox proportional hazards regression. In California, African American/Black females residing in predominantly White neighborhoods had decreased breast cancer risk compared to African American/Black females residing in predominantly Black neighborhoods (HR, 0.71; 95% CI, 0.50-0.99). Latino females residing in mixed White and Asian American/Pacific Islander neighborhoods had increased breast cancer risk (HR, 1.59; 95% CI, 1.20-2.11) in comparison to Latino females residing in predominantly Hispanic neighborhoods. In Hawaii, Japanese American females residing in multiethnic neighborhoods had increased breast cancer risk (HR, 1.49; 95% CI, 1.24-1.78) compared to Japanese American females residing in predominantly Asian American neighborhoods. Native Hawaiian patients residing in predominantly Asian American neighborhoods had increased breast cancer risk (HR, 1.23; 95% CI, 1.04-1.45) compared to Native Hawaiian patients residing in mixed Native Hawaiian neighborhoods. Our findings can inform future studies to identify specific pathways through which segregation influences cancer risk in multiethnic populations.

[object Object], [object Object], [object Object] et al.

Naunyn-Schmiedeberg's archives of pharmacology • 2025

Liraglutide, a glucagon-like peptide-1 (GLP-1) receptor agonist, is well-established for its metabolic benefits, including glycemic control and weight loss. Beyond these roles, it exhibits significant anti-inflammatory properties, though the mechanisms remain underexplored. This study investigates the anti-inflammatory effects of liraglutide in lipopolysaccharide (LPS)-stimulated RAW 264.7 murine macrophages. Results demonstrate that increasing concentrations of liraglutide suppresses LPS-elevated prostaglandin E2 (PGE2), 6-keto prostaglandin F1α (6-keto-PGF 1α , a stable prostacyclin metabolite) and thromboxane A2 (TXA2), similar to that observed with conventional anti-inflammatory agents, ibuprofen and celecoxib. Mechanistic exploration reveals that liraglutide's anti-inflammatory action is dually-modulated by cyclooxygenase (COX) and nicotinic acetylcholine receptor (nAChR) signaling. The application of non-selective, non-competitive nAChR antagonist or selective and potent α7-nAChR antagonist, mecamylamine (MEC) and methyllycaconitine (MLA), respectively, highlights the involvement of cholinergic pathways in liraglutide's activity. Based on in silico molecular docking analyses, liraglutide exhibits favorable binding affinities to COX-1, COX-2, prostacyclin synthase (PGIS), and α7nAChRs, supporting its potential multi-target anti-inflammatory effects. These findings suggest that the therapeutic potential of liraglutide may go beyond metabolic regulation and may be promising for conditions in which metabolic and inflammatory pathways converge, including inflammation and modulation of cholinergic signaling.

[object Object], [object Object], [object Object] et al.

Oncogene • 2025

Mucoepidermoid carcinoma (MEC) is the most frequently occurring salivary gland malignancy. Here, we investigated transcriptomic profiles of human fetal and adult salivary glands and MEC tumors to assess programs involved in MEC progression. Molecular and genetic analyses revealed that MEC tumors and fetal salivary glands share proliferative and developmental gene expression profiles that implicate an FGFR-p53 signaling axis in salivary MEC progression. Based on these findings, we developed a genetically engineered mouse model of advanced MEC via targeted expression of the CRTC1-MAML2 oncogene in salivary ductal cells. Specifically, CRTC1-MAML2 expression combined with p53 dysregulation in salivary ducts rewires FGF signaling to drive formation of tumors with histological and molecular features of high-grade MEC. The combined bioinformatics and mouse modeling of this study demonstrate that salivary MEC progression is underpinned by reactivation of developmental signaling programs and suggests a role for FGFR targeted therapies in the treatment of high-grade MEC.

[object Object], [object Object], [object Object] et al.

Neurological research • 2025

To explore the neuroprotective role of Myeloid-Derived Growth Factor (MYDGF) and its regulatory mechanisms involving G protein-coupled signaling pathways after cerebral ischemia-reperfusion injury (CIRI).

[object Object], [object Object], [object Object] et al.

Clinical pharmacokinetics • 2025

Tacrolimus treatment is complicated by its narrow therapeutic range and large inter- and intra-patient variability. This study aimed to develop a population pharmacokinetic model and dosing algorithm to predict an individual's dose requirement following living and deceased donor kidney transplantation.

[object Object], [object Object], [object Object] et al.

The EMBO journal • 2025

Immunoglobulin A (IgA) is essential for mucosal immunity and has been implicated in autoimmune diseases, such as IgA nephropathy. Certain pathogenic and commensal bacteria produce IgA proteases that selectively cleave IgA, potentially aiding bacterial colonization as well as suggesting therapeutic avenues for IgA nephropathy. Here, we investigate the substrate specificities of two enzymes of the M64 metallopeptidase family, the IgA protease ThomasA from Thomasclavelia ramosa and BF3526 from Bacteroides fragilis. Our structural, biochemical, and mutagenesis analyses demonstrate that ThomasA cleaves IgA through exclusive recognition of the Fab region. This mechanism is distinct from that of other antibody-specific peptidases, which typically require engagement of the Fc region. In contrast, X-ray crystal structures of BF3526 in complex with substrate and product peptides, combined with enzymology assays, show that this enzyme targets the N-terminus of pre-digested proteins, but does not act on intact IgA. These findings reveal divergent substrate recognition strategies between M64 family members, while providing new structural insights into their conserved catalytic mechanism.

[object Object], [object Object], [object Object] et al.

Scientific reports • 2025

Pest detection is vital for maintaining crop health in modern agriculture. However, traditional object detection models are often computationally intensive and complex, rendering them unsuitable for real-time applications in edge computing. To overcome this limitation, we proposed DGS-YOLOv7-Tiny, a lightweight pest detection model based on YOLOv7-Tiny that was specifically optimized for edge computing environments. The model incorporated a Global Attention Module to enhance global context aggregation, thereby improving small object detection and increasing precision. A novel fusion convolution, DGSConv, replaced the standard convolutions and effectively reduced the number of parameters while retaining detailed feature information. Furthermore, Leaky ReLU was replaced with SiLU, and CIOU was substituted with SIOU to improve the gradient flow, stability, and convergence speed in complex environments. The experimental results demonstrate that DGS-YOLOv7-Tiny performs excellently on the tomato leaf pest and disease dataset, with 4.43 million parameters, 10.2 GFLOPs computational complexity, and an inference speed of 168 FPS, achieving 95.53% precision, 92.88% recall, and 96.42% mAP@0.5. The model delivered faster inference and reduced computational requirements while maintaining competitive performance, offering an efficient and effective solution for pest detection in smart agriculture with substantial theoretical and practical value.

[object Object], [object Object], [object Object] et al.

Clinical and translational science • 2025

IMC-002, an anti-cluster of differentiation 47 (CD47) antibody, was developed as a fully human immunoglobulin (Ig) G4 monoclonal antibody targeting CD47. IMC-002 blocks CD47-signal regulatory protein alpha (SIRPα) interactions, increasing the phagocytosis of cancer cells by macrophages. This study established a target-mediated drug disposition (TMDD) pharmacokinetics (PK) model for IMC-002 to explain the characteristics of the neonatal Fc receptor (FcRn) of IgG based on phase I a trial data to perform model-informed optimal dosing for a phase I b study. The PK data were obtained from a phase I a dose escalation study on 12 patients with advanced solid tumors. Each patient received multiple doses (5, 10, 20, or 30 mg/kg) via intravenous infusion over 3 h every 2 weeks. The IMC-002 PK data were analyzed using NONMEM software (version 7.5) using the first-order conditional estimation method with interaction (FOCEI). The semi-mechanistic TMDD model explained the nonlinear PK properties of IMC-002, including FcRn recirculation. Weight, sex, and age were investigated as covariates using stepwise covariate testing. No statistical covariates were identified. Simulations were performed to determine the optimal dosing regimen for patients with solid tumors that extend the administration period and maintain the mean concentrations above the minimum effective concentration (MEC). The ideal regimen of 20 mg/kg every 3 weeks ensured that the mean plasma concentration remained above the MEC throughout the dosing interval. Our study developed a robust TMDD PK model for IMC-002, which provides the rationale for a dose regimen in a phase 1 b study.

[object Object], [object Object], [object Object] et al.

Naunyn-Schmiedeberg's archives of pharmacology • 2026

Clozapine, the only drug approved by the FDA for treatment-resistant schizophrenia, operates within a narrow therapeutic range (0.35-0.60 mg/mL) and requires titration from 12.5 to 300-450 mg daily in two divided doses. Non-adherence, driven by adverse effects and logistic barriers, often manifests as missed or delayed doses. Clozapine's forgiveness, i.e., its capacity to sustain efficacy despite dosing errors, is poorly quantified. This study simulated the pharmacokinetics (PK) of clozapine to assess forgiveness across dosing scenarios. A one-compartment PK model (CL = 8.3 L/h, V d  = 2.8 L/kg, Ka = 0.69 h⁻ 1 , F = 0.27) with variability (22% CL, 14% V) was developed, and steady-state metrics for 200 mg twice-daily dosing were validated against literature reference. Simulations (200 mg, q12h) over 150 h for 500 subjects tested perfect adherence, 1-8 consecutively missed doses, and 1-10-h delays at steady state. Forgiveness was measured as AUC and time above minimum effective concentration (0.35 mg/L), expressed as percentages retained relative to perfect adherence. The perfect adherence yielded 0.44-0.62 mg/L, matching reference values. One missed dose reduced troughs to ~ 0.3 mg/L; eight consecutively missed doses dropped levels to near zero, slashing AUC and time above MEC to ~ 0%. A 10-h delay cuts AUC to 29% and time above MEC to 57%, with recovery by 150 h. Clozapine shows low forgiveness for missed doses, necessitating strict adherence but moderate delay tolerance, supporting flexible timings. Re-titration post-48 h is critical, guiding clinical management.

[object Object], [object Object], [object Object] et al.

American journal of physiology. Endocrinology and metabolism • 2025

Type 2 diabetes is considered the pandemic of the 21st century. The pathogenesis of diabetes is complex and multifactorial, and its understanding is crucial for its prevention and treatment. Nevertheless, the pathogenesis of β-cell dysfunction remains unclear. This gap in knowledge could be related to a lack of appropriate preclinical models of type 2 diabetes. Current animal models, mostly genotypic and monogenic, do not fully reflect the pathophysiology of type 2 diabetes or associated organ damage. Therefore, a new preclinical model is needed. In this study, we present a non-genotypic animal model of diabetes that combines obesity and the treatment with the diabetogenic immunosuppressor tacrolimus (TAC). Sprague-Dawley male rats were fed with a high-fat diet (HF) to establish obesity and metabolic syndrome. Then, TAC was administered for 6-9 mo to induce chronic hyperglycemia. These animals showed a decrease in Langerhans islets area and number, and development of islet fibrosis. There were also changes in pancreatic insulin and glucagon content, imbalance in the α/β-cell ratio and decrease in the expression of essential transcription factors V-maf musculoaponeurotic fibrosarcoma oncogene homolog A (MAFA), forkhead box protein O1 A (FOXO1A), pancreatic and duodenal homeobox 1 (PDX-1), and neuronal differentiation 1 (NEUROD1). Altogether, this could be a more appropriate animal model to study the pathophysiology of diabetes and, eventually, related organ damage. NEW & NOTEWORTHY After 6-9 mo of treatment, animals presented fasting and chronic hyperglycemia in intraperitoneal glucose tolerance test (IPGTT). Pancreas of obese-diabetic animals showed a decrease in Langerhans islets area and number, and development of islet fibrosis. Islets showed a decrease in insulin content and in β-cells proportion, and an increase in α cells proportion. There was also a decrease in the expression of transcriptional factors essential for β cells: V-maf musculoaponeurotic fibrosarcoma oncogene homolog A (MAFA), Forkhead box protein O1 A (FOXO1A), pancreatic and duodenal homeobox 1 (PDX-1), and Neuronal differentiation 1 (NEUROD1).