Search Results (10,410)

The inadequacy of traditional preclinical oncology models, specifically two-dimensional (2D) monolayer cultures and murine in vivo systems, in predicting human drug responses has led to the development of patient-derived organoids (PDOs) and microfluidic organ-on-chip (OoC) technologies. These innovations represent significant recent methodological advancements in the field of cancer research. This review synthesizes the biological rationale, technical principles, and translational applications of PDO–OoC integration, with an emphasis on recent clinical validation studies, AI integration, and post-FDA Modernization Act 2.0 regulatory evolution—areas that have not been comprehensively addressed in prior reviews. We examined the predictive limitations of 2D models, organoid generation, and ToC engineering principles. The synergistic integration of organoids into chip-based systems, extended into multi-organ “Body-on-a-Chip” architectures, is presented as a unifying framework that combines patient-specific biological fidelity with dynamic microenvironmental control. We further reviewed the research applications and early clinical validation studies of high-throughput drug screening, immuno-oncology modeling, and patient-specific drug response prediction across multiple tumor types. Clinical validation studies have reported moderate correlations (r ~ 0.4–0.6) between organoid responses and outcomes, indicating partial predictive capacity. Despite this progress, clinical translation remains constrained by standardization and reproducibility deficits, biomaterial limitations (e.g., PDMS drug absorption and Matrigel batch variability), and regulatory ambiguities within the evolving FDA Modernization Act 2.0. Finally, we discuss the emerging integration of artificial intelligence, including transfer learning-based drug response prediction and real-time organoid avatar systems in clinical trials, as a pathway toward closed-loop individualized functional precision oncology. Organoid and tumor-on-chip platforms have advanced toward clinical utility, although barriers remain. Full article

Practical training on embedded systems is a very good way to assess the skills of students in firmware development, real-time programming and hardware–software co-design. However, many low- and middle-income institutions still use closed, proprietary boards, such as the Altera DE series, which hide the physical behavior. To address this, a low-cost, fully reconfigurable Microcontroller Test Board (MTB) was designed and technically validated for application in undergraduate laboratories. The MTB is manufactured as a one-sided through-hole PCB via toner transfer and FeCl3 etching, with a PIC18F2550 in-circuit programmer and support for 28- and 40-pin targets including the PIC16F877A. The MTB integrates six functional blocks, providing five canonical input/output modalities on a single regulated 5 V/0.56 A rail: (1) an ICSP programmer; (2) a multiplexed seven-segment display; (3) an 8 × 8 LED matrix; (4) a matrix keypad with LCD calculator; and (5) a 10-bit ADC stage. All subsystems operate with a flicker-free refresh rate > 122 Hz. The total board power consumption is 6.72 W at 12 V DC, with the linear regulator dissipating 3.92 W under full load. Probe points allow for signal transparency. Models for regulator dissipation, ADC quantization, multiplex refresh duty cycle, LED current limiting, oscillator timing, matrix-scanning latency, LCD timing, keypad response and Fe³⁺ with Cu etch kinetics were first-principles. Bench measurements agree with model predictions to less than 3% error, and confirm worst-case design margins. The MTB provides analytical rigor with accessible hardware to enable measurable experiments in microcontroller labs. The platform is cost-effective, with a material cost of less than USD 18. Each signal is routed to probe points, providing significant cost reduction from commercial trainers, and a transparent hands-on laboratory resource for engineering curricula with limited resources. Full article

This study reports on a new one-pot copolymerization process by simultaneous oxidative disulfide and β-thioester formation by reacting bifunctional monomers, 3,6-dioxa-1,8-octane-dithiol (DODT) with diacrylates, poly(ethylene glycol) diacrylate (PEGDA) and 1,6-hexanediol diacrylate (HDODA), in the presence of N,N,N′,N″,N″-pentamethyldiethylenetriamine (PMDETA), as the same base catalyst for both reactions, in air at room temperature with short reaction times. The resulting random copolymers consist of disulfide linkages between DODTs and β-thioester units formed by thiol-ene Michael addition click reaction. With a stoichiometric DODT/diacrylate feed ratio, diacrylate-telechelic copolymers are obtained in a single-step polymerization reaction. The T_gs of the P(DODT-co-PEGDA) copolymers are nearly constant at around −53 °C, while it decreases with increasing HDODA content in the P(DODT-co-HDODA) copolymers. Reductive degradation with thiols, such as 2-mercaptoethanol and dithiothreitol, led to chain scission via the disulfide–thiol exchange reaction. Treatment with NaOH solution resulted in further degradation by hydrolysis of the β-thioester units. These results indicate that these novel copolymers are fully degradable under mild conditions. This new process, applying simultaneous thiol oxidation and thiol-ene reactions, enables to prepare a large variety of sulfur-containing end-functional degradable copolymers useful for a broad range of advanced application possibilities. Full article

Nowadays, industries are increasingly developing advanced materials that combine high durability, sustainability, and ease of fabrication. Aramid fibers, particularly Kevlar, are widely used in engineering applications due to their excellent mechanical properties, such as high tensile strength, impact resistance, and thermal stability. At the same time, growing concerns over environmental impacts have led to increased interest in the reuse of waste materials and the development of sustainable composite systems. In this paper, we review experimental studies on Kevlar-based composites, with particular emphasis placed on composites manufactured from waste Kevlar fibers. Particular attention is given to the influence of fiber fragmentation and surface modification performed using polyvinyl butyral (PVB) on mechanical properties. We also cover material characteristics, manufacturing techniques, and the mechanical behavior of various composite systems. We primarily focus on analyzing the methodologies and research results of studies in the literature to enable future researchers to verify the quality of existing research. Full article

Transcranial magnetic stimulation (TMS) enables non-invasive, focal modulation of cortical circuits by inducing electric currents in the brain through electromagnetic induction, thereby influencing neuronal excitability and synaptic plasticity. High inter- and intra-individual variability has led, however, to moderate efficacy and reproducibility of stimulation and treatment protocols, motivating a shift toward brain-state-dependent stimulation. Over the past decade, real-time phase-triggered EEG-TMS has established the oscillatory phase—particularly focusing on the sensorimotor mu rhythm—as a key determinant of cortical excitability and plasticity modulation. The field, however, remains largely confined to univariate, sensor-space analyses of local mu-rhythm phase, missing large-scale network dynamics. Recent advances in online EEG source reconstruction and multivariate machine and deep learning (ML/DL) approaches have begun to move beyond local phase toward whole-brain, network-level state estimation, achieving encouraging preliminary accuracies in predicting trial-by-trial cortical excitability, with promising applications in network-dysregulation conditions such as chronic pain. Integrating source-space reconstruction and individual biological variability, and adaptive ML/DL pipelines into closed-loop frameworks promises to move beyond generic stimulation protocols toward selective, network-targeted neuromodulation tailored to the individual’s dynamic brain state. Against this background, this review provides a critical overview of current achievements and limitations, while highlighting emerging methodological directions toward fully brain-state-adaptive and network-targeted EEG-TMS. We further present an illustrative use case of adaptive EEG-TMS for pain modulation, where treatment responses remain heterogeneous and the relevant dynamics are distributed across networks, and which therefore stands to gain most from individualized, network-targeted protocols. Full article

In this study, we present the development of selective-area porous GaN (SPG) microarrays integrated with embedded quantum dots (QDs) for next-generation display applications. We report a dielectric hard mask-based architecture that withstands high etching voltages without suffering structural damage, overcoming the limitations of conventional polymer masks. This process provides precise control over both vertical and lateral pore propagation, achieving an effective pixel size of 15 μm and realizing an ultrahigh-resolution (>1270 ppi) display structure. Furthermore, optical performance measurements confirmed that the obtained array exhibited an excellent color gamut, reaching 119.2% of the NTSC and 95.6% of the Rec. 2020 standards. Therefore, the present dielectric hard mask-based patterning technology provides an effective solution for achieving both process stability and high-resolution pixel structures, enabling the fabrication of high-performance micro-LED displays with full-color capabilities. Full article

The increasing demand for sustainable outdoor lighting has accelerated the development of solar-powered lighting systems. However, conventional solar lamps typically employ fixed illumination levels and simple day–night switching mechanisms, resulting in inefficient battery utilization and limited adaptability to changing environmental conditions. This study proposes a TRIZ-guided intelligent solar-powered lighting system that integrates photovoltaic energy harvesting, adaptive pulse-width modulation (PWM)-based illumination control, ultrasonic sensing, wireless communication, and embedded control into a unified standalone platform. The TRIZ contradiction matrix was employed during the conceptual design stage to systematically resolve key engineering contradictions involving illumination performance, energy efficiency, hardware complexity, battery lifetime, and user convenience. The proposed prototype was developed using an AT89S51 microcontroller to coordinate battery charging protection, environmental sensing, adaptive brightness regulation, and manual wireless operation. Experimental validation demonstrated stable photovoltaic charging with a regulated battery charging voltage of 14.4 V, reliable execution of embedded control functions, seamless transition between manual and autonomous operating modes, and adaptive LED brightness regulation according to real-time environmental conditions. The integrated PWM control strategy reduced unnecessary energy consumption by dynamically adjusting illumination intensity based on object detection rather than maintaining constant full-power operation. The experimental results further verified the feasibility of combining software-driven adaptive control with renewable energy harvesting to achieve intelligent energy management without increasing hardware complexity. Overall, the proposed system demonstrates that the integration of TRIZ-based systematic innovation with embedded intelligent control provides a practical, energy-efficient, and cost-effective solution for autonomous outdoor lighting. The proposed architecture offers valuable engineering insights for future smart lighting applications in off-grid infrastructure, sustainable communities, and smart city environments. Full article

Background/Objectives: It remains unclear whether sleep deprivation induces constipation. This study aimed to evaluate the intervention effects of Bifidobacterium longum 151-1 and BN-BB01 on constipation induced by sleep deprivation in mice, and to explore the underlying mechanism from the perspective of the gut-brain axis, so as to provide a theoretical basis for probiotic intervention in sleep-related gastrointestinal dysfunction. Methods: A mouse model of sleep deprivation was established via the multiple platform water environment method. Through the detection and analysis of relevant indicators, the effects of sleep deprivation on intestinal function in mice were systematically investigated, and the changes in various indicators of mice after Bifidobacterium longum intervention were observed. Results: Sleep deprivation significantly impaired intestinal motility and intestinal mucosal barrier function in mice, reducing fecal water content by approximately 21.4%, prolonging the time to first black stool defecation by about 57 min, and decreasing goblet cell count by nearly 50%. In terms of gastrointestinal hormones and neurotransmitters, sleep deprivation significantly downregulated the levels of excitatory gastrointestinal hormones (MTL, GAS) and excitatory neurotransmitter SP by 22.3%, 14.8%, and 16.2%, respectively. Additionally, sleep deprivation promoted the secretion of the inhibitory neurotransmitter VIP and stress hormone CORT, with their levels increased by 53.1% and 23.8%, respectively. The levels of neurotransmitters including 5HT and GABA were reduced by 15.6% and 23.2%, which ultimately led to a decrease in short-chain fatty acid (SCFA) levels. Intervention with Bifidobacterium longum significantly reversed the above abnormalities. It elevated SCFA levels and upregulated the expression of SCFA receptors GPR41 and GPR109A, promoted the expression of BDNF and GDNF, inhibited HDAC activity, increased the abundance of the beneficial bacterium Akkermansia, and reduced the abundance of the harmful intestinal bacterium Alistipes. Conclusions: Bifidobacterium longum can alleviate sleep deprivation-induced constipation in mice by regulating gut-brain axis function, optimizing intestinal flora structure, modulating the secretion of gastrointestinal hormones and neurotransmitters, and repairing the intestinal mucosal barrier. This study provides a solid theoretical basis for the clinical application of probiotics in the intervention of sleep-related gastrointestinal disorders. Full article

The rapid expansion of aquaculture has led to a substantial increase in the generation of organic waste, nutrient-rich effluents, and residual biomass, creating significant environmental challenges while simultaneously providing opportunities for resource recovery within the circular bioeconomy. Environmental biotechnology offers a broad range of solutions for transforming these waste streams into valuable products, including renewable energy, fertilisers, biochar, microbial biomass, and other bio-based resources. However, the practical implementation of these technologies in floating aquaculture systems remains fragmented due to the lack of integrated engineering approaches. This review critically analyses the current state-of-the-art in environmental biotechnology pathways for aquaculture waste valorisation, covering biological, physicochemical, and thermochemical technologies applicable to floating production systems. The assessment covers anaerobic digestion, composting, insect bioconversion, microalgae cultivation, integrated multi-trophic aquaculture, nutrient recovery, hydrothermal carbonisation, and pyrolysis, together with their technological maturity, offshore applicability, and contribution to circular resource recovery. Beyond reviewing existing technologies, this paper proposes the “Circular Floating Aquaculture Systems Engineering Integration Framework” (CFAS-EIF), a systems engineering concept that integrates floating production infrastructure, waste collection, resource recovery technologies, digital management, and circular value chains into a unified architecture. The framework illustrates how environmental biotechnology can be implemented through coordinated engineering integration, enabling the transition from waste management to resource valorisation in offshore aquaculture. The proposed framework provides researchers, engineers, and industry stakeholders with a comprehensive reference for designing next-generation aquaculture platforms that support sustainable resource management, climate change mitigation, and a circular bioeconomy. Full article

Reference building models can serve as a common baseline for research investigations and evaluations of design choices. Despite lighting being one of the largest single users of electricity in commercial buildings, lighting systems in reference buildings are commonly modeled as maximum building-type or space-type power densities defined in building energy codes. Further, energy use is typically estimated by modulating the lighting power over the course of a day via simple occupancy schedules that may not accurately portray human behavior or the performance of occupancy-based lighting controls. While this approach is suitable for some use cases, it is not adequate for exploring the impact of luminaire selection, space-specific control strategies, and occupant behavior. This paper presents the development of a high-fidelity reference model of a medium office building that includes interior architecture, an occupant distribution model, and a detailed lighting design intended to support more realistic evaluations of lighting control strategies and approaches to modeling occupant behavior. The model includes 13 market-representative LED luminaires, meets occupant lighting needs by complying with applicable recommended practices defined by the Illuminating Engineering Society (IES), and meets energy code power density and control strategy requirements in ANSI/ASHRAE/IES 90.1-2019. The luminaire-level connected load (26,037 W) of the lighting layout is lower than what would be estimated by simple building-type (34,304 W) and space-type (31,200 W) power density methods. The model includes 12 space types and 107 rooms with workstations for 268 occupants. The interior architecture varies on each of the three floors to introduce room and space-type diversity that further exposes the impact of design choices and occupant behavior. The model has already been used as a baseline for research on circadian lighting design strategies, lighting–HVAC data integration, and whole-building life-cycle assessments—demonstrating its utility as a common reference for a range of lighting research. Full article

Artificial intelligence (AI) is increasingly acknowledged as a strategic asset for enhancing innovation and competitiveness among small and medium-sized enterprises (SMEs). Nevertheless, limited research has focused on the experiences of women entrepreneurs regarding AI adoption within established SMEs, particularly in emerging economies. This study investigates the knowledge, attitudes, skills, perceived barriers, and business-process applications associated with AI adoption among women entrepreneurs in Chile. Employing an exploratory multiple-case study design, semi-structured interviews were conducted with fourteen women leading established SMEs across various industries. The cross-case analysis indicates that resilient leadership, social intelligence, and managerial knowledge were frequently mentioned characteristics alongside diverse patterns of AI adoption. The findings reveal that participants identified strategic-planning and management-control knowledge, resilient leadership attitudes, and social intelligence skills as recurring competencies associated with AI adoption into business activities. Participants reported utilising AI primarily to support customer relationship management (CRM), while also describing limited AI readiness as a constraint associated with learning needs and experiences of AI adoption. To distinguish between AI adoption and broader digitalisation and automation, the study defines the following adoption profiles: non-adopter, AI experimenter, substantive AI adopter, and integrated AI adopter. Further cross-case analysis suggests that participants experienced knowledge, attitudes, and skills as mutually reinforcing during AI adoption rather than as isolated competencies. This study contributes to the literature by providing an empirically grounded understanding of how a group of established women entrepreneurs perceive and experience AI adoption in their SMEs. Instead of identifying causal determinants of successful adoption, the findings highlight recurring patterns and relationships that may inform future theory development and comparative research on AI adoption in entrepreneurial contexts. The study also offers practical insights for policymakers, educators, and SME support organisations aiming to enhance AI capabilities among women-led businesses. Full article

The main goal of this work is to develop an algorithm for visual presentation of information through light in three-dimensional space. The problem of controlling a large number of digital outputs at the same time using a very small number of control signals is considered. A single-chip microcontroller Arduino Atmega 328 is used as a processor in a control unit (CU). The control unit is implemented based on shift registers. A physical structure of LEDs is also implemented as an executive device (LED matrix). Remote control is done using a Bluetooth module, and software (for controlling the Arduino chip) is added to control the LED matrix. The matrix consists of a total of 125 LEDs, forming a structure of 5 × 5 × 5 LEDs representing a three-dimensional “screen” through which information is presented. The software environment for developing applications specifically for Arduino is used for the software, namely Arduino IDE. The proposed implementation achieves a low-cost 3D display architecture controlled by a voice-controlled volumetric interface. Based on the shift registers used, effective control over the entire 3D visualization system is achieved. Full article

Industrial wastewater in Islamabad has led to serious environmental and human health impacts due to the release of endocrine-disrupting chemicals (EDCs) and volatile organic compounds (VOCs) in the water bodies. The advent of wastewater treatment technologies has led to the development of novel approaches like the use of an electrocatalyst for efficient and safe removal of toxic contaminants from the wastewater. In this study, an efficient tri-metallic NiCoFe₂-LDH electrocatalyst was prepared by optimizing the metal ratios in the synthesis process using a co-precipitation method. The catalyst was characterized by various tools such as UV-visible spectroscopy, SEM, EDX, and FTIR. A three-electrode electrochemical system (counter, reference, and working electrodes) was used for electrocatalytic activity and simultaneous removal of EDCs from the wastewater. Wastewater samples were collected from selected locations along the industrial discharge channel (Nullah Lai) in the industrial zone of Islamabad city. The physio-chemical analysis showed significant water pollution, including EDCs, VOC, and heavy metals. The electrochemical treatment using NiCoFe₂-LDH demonstrated high efficiency across different scan rates (5 to 50 mV/s) and voltages (−0.02 to 1.6 V); for instance, at 1.6 V, the current density rises from 10 mA/cm² in Scan 5 to 50 mA/cm² in Scan 50. Furthermore, COD levels were significantly reduced by 74.9%, from 665.6 mg/L to 166.4 mg/L, after electrochemical treatment with NiCoFe₂-LDH. GCMS analysis of organics revealed that the electrochemical process was effective in reducing several EDC- and VOC-related peak groups. Overall, this study highlights the potential of the electrochemical approach for treating EDC-contaminated wastewater and its applicability as a sustainable solution for industrial wastewater treatment. Full article

The Fe–6.5 wt.% Si alloy is a promising soft magnetic material for electric motor applications owing to its high electrical resistivity and low core loss. However, the intrinsic brittleness of this alloy precludes fabrication of thin laminates using conventional rolling processes. Laser Powder Bed Fusion (LPBF) has therefore been considered as an alternative manufacturing route, offering both geometric flexibility and the inherent advantages of additive manufacturing. Nevertheless, successful LPBF processing of Fe–6.5 wt.% Si has remained challenging due to its high-silicon content. In this study, the Fe–6.5 wt.% Si alloy was modified by introducing 1 wt.% Cr, and LPBF process variables were optimized to yield defect-free parts. The effect of Cr addition on suppressing the disorder–order phase transformation during solidification was investigated through Thermo-Calc^® thermodynamic simulations and quantified via X-ray diffraction phase analysis. Crack morphology analysis from optical micrographs revealed a marked reduction in both solidification and liquation cracks, attributed to the role of Cr in mitigating silicon segregation and consequently lowering the fraction of ordered phases. Preheating the build plate to 200 °C was found to effectively eliminate vertical cracks by reducing thermal stresses within the parts; however, a limited number of horizontal cracks initiated at the sample edges and propagated inward, likely due to elevated thermal gradients at the perimeter. To address this issue, sacrificial walls were introduced at distances of 1.0 mm and 0.2 mm from the cube edges, locally reducing the cooling rates and effectively increasing the primary dendrite arm spacing (PDAS). The reduced cooling rate also led to lower lattice misorientation, confirmed by electron backscatter diffraction (EBSD), and a significant decrease in the crack length from ~2 mm to ~0.7 mm. Full article

Wellness resorts often market natural scenery without knowing whether a structured way of attending to it changes guests’ immediate experience. We examined cloud noticing and forest-therapy elements as two low-infrastructure forms of nature-directed hospitality programming. This exploratory, randomised field study reports outcomes from three facilitator-led arms of a four-condition parent experiment whose service-evaluation findings were published separately. The parent experiment allocated 120 adult resort guests equally, by individual manual lottery, to cloud noticing, forest-therapy elements, an active-control session, or an ordinary stay. The present complete-case dataset comprised 84 participants with linked measurements immediately before and after an organised activity: cloud noticing (n = 29), forest-therapy elements (n = 28), and active control (n = 27). The ordinary-stay group was outside the analytical scope because no additional session, and therefore no matched session-level measurement interval, was available. Linear mixed-effects models estimated group-by-time contrasts for state anxiety, positive and negative affect, and subjective restoration; perceived stress, connectedness with nature, six activity-specific ratings, resting pulse, and blood pressure were secondary or descriptive variables. Both nature-focused sessions showed more favourable short-term psychological patterns than the active control, with the clearest contrasts for subjective restoration. Cloud noticing was associated descriptively with awe, mental spaciousness, and perspective distancing, whereas forest-therapy elements were associated with sensory immersion, bodily presence, grounding, and the greatest increase in nature connectedness. Pulse and blood pressure are reported only as physiological field descriptors. The earlier companion publication examined experience quality, satisfaction, novelty, added value, and recommendation intention; none of those endpoints are reanalysed here. Applicability is limited by the single-resort setting, lack of blinding, complete-case analysis, absence of preregistration, and lack of follow-up. Full article