Design and development of a low-cost industrial prototype of spot-specific spraying system for potato weed detection using deep learning

Abstract Ionizing radiation has emerged as a potential limiting factor for superconducting quantum processors, inducing quasiparticle bursts and correlated errors that challenge fault-tolerant operation. Atmospheric muons are particularly problematic due to their high energy and penetration power, making passive shielding ineffective. Therefore, monitoring the real-time muon flux is crucial to guide the development of alternative error-correction or protection strategies. We present the design, simulation, and first operation of a cryogenic muon-tagging system based on Kinetic Inductance Detectors (KIDs), developed as a stand-alone cryogenic particle-tagging module for superconducting quantum processors. The system consists of two KIDs arranged in a vertical stack and operated at ∼20 mK. Monte Carlo simulations based on Geant4 guided the prototype design and provided reference expectations for muon-tagging efficiency and accidental coincidences due to ambient γ-rays. We observed a muon-induced coincidence rate among the top and bottom detectors of (192 ± 9)×10 -3 events/s, in excellent agreement with the Monte Carlo prediction. The prototype achieves a muon-tagging efficiency of about 90% with negligible dead time. These results demonstrate the feasibility of operating a muon-tagging system at millikelvin temperatures and represent a key step toward the integration of cryogenic veto systems with multi-qubit chips to mitigate muon-induced errors.

While physical activity (PA) has protective effects in mitigating dementia progression among individuals with mild cognitive impairment (MCI), suboptimal exercise adherence remains a critical barrier. Mobile health technology, when integrated with preference-based personalization strategies, may enhance adherence through tailored interventions. However, empirical evidence validating the feasibility of exercise preference-driven mobile solutions for populations with MCI remains limited. To develop and evaluate a mobile exercise management application specifically designed for community-dwelling older adults with MCI that incorporates personalized preference-driven features. A three-phase mixed-methods study was conducted: (1) Formative research: key design elements of the exercise application were identified based on evidence from existing interventions and users' exercise preferences (self-selected by participants). (2) Prototype design and development: an iterative methodology was employed to design, develop, and test the application's functionality. (3) Usability evaluation: A 4-week usability evaluation was conducted with community-dwelling older adults with MCI using the application. Twelve participants (age = 70.60 ± 3.21; MoCA = 21.67 ± 2.23) completed the 4-week usability evaluation. The application demonstrated good usability (SUS = 77.73 ± 6.73), with optimization targets identified in functional integration (item 5 converted score = 2.67 ± 0.49) and initial technical support needs (item 7 converted score = 2.33 ± 0.49). The median exercise adherence (exercise goal completion rate) among participants was 100.00% (87.48, 100.00), with 75% (9/12) of participants achieving or exceeding their weekly goals. The exercise preference agreement rates exhibited considerable variability: environment-100.00% (93.55, 100.00) and modality-97.83% (63.40, 100.00). While there was a strong positive correlation between the environment and modality preference agreement rates (Spearman rho = 0.817 [p = 0.001]), there was no association between preference agreement rates and adherence (p > 0.05). Users highly valued the application's real-time monitoring, motivational features, and intuitive interface, with most reporting no significant usability issues. This 4-week, single arm usability study demonstrates that preference-driven mobile exercise management is not only feasible but also holds promise for promoting exercise engagement in community-dwelling individuals with MCI. User feedback identified critical optimization needs in exercise type diversity and initial technical support. While synergistic relationships between exercise environment and modality preference agreement rates were evident, the absence of a correlation between preference agreement rates and exercise adherence underscores the need for longitudinal studies to explore dynamic preference-behavior interactions. Technology adoption barriers (e.g., self-efficacy, digital literacy) and memory-associated challenges necessitate multimodal objective monitoring. These findings highlight two implementation priorities: prioritizing accessibility in exercise environments and incorporating personalized adaptation strategies to address exercise pattern variability.

Acute care providers lack an easy way to assess their prescribing practices and track future related care for their patients. Thus, we conducted design evaluations and subject matter expert (SME) design sessions in a user-centered design (UCD) approach to develop an audit and feedback tool that provides individualized, scalable prescribing feedback to clinical providers about their antibiotic and nonsteroidal anti-inflammatory drug (NSAID) prescriptions in unplanned care settings (e.g., emergency department and urgent primary care). A UCD approach was conducted with 11 individual interviews through two rounds of formative testing focusing on interface design efficiency, effectiveness, and visualization interpretability. We conducted several design sessions with SMEs, prescribers in emergency and primary care medicine, where the design team asked the SMEs to comment and do a walk-through with various design prototypes for the tool and then further iterate new designs. Feedback about different user interface designs was obtained from future tool users in usability evaluation sessions where a provider interacted with the prototype through think-aloud guided by a semi-structured interview outline. Through two rounds of usability evaluations, key usability issues were identified with the navigation, language, and interpretation of the data presented. This led to substantial interface design changes prior to implementation that improved usability and usefulness as evidenced by a decrease in the number of usability issues found during the second round of evaluation. Participants appreciated the concept and usefulness of the tool presented; however, during usability sessions, they identified important optimizations, clarifications, and changes for improvement. Key generalizable findings include user preferences for non-judgmental framing of prescribing, and desire for intuitive presentation and summarization of recent care delivered to support actionable feedback. Required changes during UCD underscore the importance of this type of usability evaluations during tool ideation and development.

Asymmetric alkylation is widely used for the construction of α-stereogenic carbonyl compounds, yet existing catalytic protocols typically suffer from several issues: (1) a limitation to π-activated electrophiles, (2) the need for unsatisfying catalyst loadings, (3) a lack of catalyst recyclability, and (4) sophisticated catalyst structures requiring multi-step syntheses. Herein, an efficiently accessible, air-stable bifunctional Cu(II)/imidazolium catalyst (prepared over four steps without chromatographies in 74% yield) is reported that enables highly enantioselective α-alkylations of 1,3-dicarbonyls with unmet productivity (TON up to 1740). The catalyst exhibits broad electrophile compatibility, efficiently engaging π-activated and non-π-activated alkylation agents. Remarkably, stereoretentive allylation with (E)- and (Z)-configured allylbromides was achieved. The catalyst can be recycled over multiple cycles (10+) without loss of efficiency by a simple protocol. EPR proves formation of a Cu(II)-enolate as resting state, for which detailed DFT calculations show that it is structurally anchored by hydrogen-bonding to the imidazolium C(2)H. This feature is essential for stereodifferentiation of both enolate faces. A continuous mechanistic shift from SN1-like to SN2-type pathways is likely, depending on the electronic properties of the electrophile. This new alkylation concept allows for high practicality, combined with broad applicability and might serve as design prototype for future alkylation catalysts.

(1) Background: Currently, there is a problem of prompt determination of fat and protein content in the milk–air mixture of milking machines. (2) Methods: A design of a sensor prototype is proposed, combining measurements of light scattering (scatterometry) and fluorescence (fluorometry) to determine the component composition of the milk–air mixture formed during milking. (3) Results: An optical and electronic circuit of a flow sensor has been developed, using four sources of optical radiation: blue, green and red semiconductor lasers (light scattering in milk) and a UV LED (milk fluorescence), as well as an axial photodiode array for recording the light scattering indicatrix and the fluorescence intensity of the milk–air mixture. The use of three laser sources in the scatterometric circuit allows for the determination of the fat content in milk with an error of 0.05%, which is better than all currently known analogs. The developed sensor enables the detection of counterfeit milk containing palm oil instead of milk fat. It operates reliably in a temperature range of 5–35 °C and at milk flow rates of up to 100 mL/sec. (4) Conclusions: The sensor is capable of transmitting real-time data on the fat and protein content of milk to an RS-232 serial port, enabling integration into milking robots and automated milking systems.

Futures thinking is a crucial skill for organisations navigating volatile, uncertain, complex, and ambiguous environments. Nonetheless, some researchers and practitioners argue that the insights futures thinking can yield appear obvious, trivial, or overly abstract and stress the need to move towards futures making. This paper examines how early-stage and punctuated prototyping can enhance futures making within urban transformation processes, supporting the co-creation, experimentation and decision-making on futures from local stakeholders. Drawing from the case study of T-Factor, a four-year project aiming to envision and prototype future uses in urban spaces, we find that two essential dimensions of prototyping—filtering and manifesting—can aid stakeholders in thinking about futures throughout the design process and, eventually, enable futures making. The content analysis of our data shows how these two dimensions contribute to futures making by facilitating the materialisation of more concrete, actionable, and engaging representations of the future that can be experienced, performed, assessed, and refined. The paper concludes by proposing practical recommendations for organisations and communities aiming to envision and realise sustainable urban futures through prototyping.

Towards self-explaining spiral tunnels: road-prototype-based facility design and visual-perception fluid assessment.

Optimized design and preliminary prototype test of a superconducting magnet for 170 GHz gyrotron

Health inequality is critical in general practice education yet is often taught through lecture-based formats that struggle to connect theoretical concepts with real-world application. Empathy and care navigation skills are essential to primary care. Game-based learning offers a dynamic and engaging alternative for trainees and students. The aim of this study was to use design methodologies to transform traditionally delivered didactic teaching into an interactive, game-based lesson. This thereby enhanced learners' understanding of social determinants of health, empathy, and community care navigation. Using the Double Diamond framework (4Ds: Design, Define, Develop, and Deliver), human-centred design and iterative prototyping was applied. A scoping review of gamification and educational principles was applied. Next, a character-driven strategy game was developed whereby players make a series of decisions, choosing between health and important life milestones. Subsequently, a low-fidelity prototype was developed and focus group-tested with educators, trainees, and multidisciplinary healthcare team (n = 20). Feedback informed refinements of character narratives, visuals, and game mechanics. The prototype was delivered in four 1-hour small-group workshops, and feedback collected via semi-structured qualitative surveys and thematically analysed. Ninety out of 147 participants (61.2%) responded, with a mean rating of 4.7/5 stars; 68.9% would 'Highly recommend' it to colleagues. Thematic analysis demonstrated high engagement, competencies' attainment, and active reflection on personal practice such as 'importance of adjusting care plans to patients' or 'opened my eyes to the need for social prescribing'. CONCLUSION: This project demonstrates the use of design engineering concepts as a flexible and effective method of reinvigorating training material and enhancing learners' ability to apply learnt professional values and skills to real-life daily scenarios.

Veterans enrolled in both Veterans Affairs (VA) health care and Medicare Part D can choose to obtain medications through the VA, Medicare Part D, or both, with each option differing in cost, coverage, and coordination of care. Poorly informed choices can lead to veteran frustration when their expectations are not met, delays in medication access, and increased risks. This study aimed to develop a decision aid (DA) to help veterans with diabetes make informed choices about medication sourcing (ie, whether to fill medications through VA health care only, Part D only, or both). DA development was guided by the International Patient DA Standards and the Ottawa Decision Support Framework. Interviews with veterans and care partners informed the prototype design. Alpha testing with 18 end users (mostly veterans) and 12 stakeholders (pharmacists, doctors, payers, Medicare counselors, and others) assessed comprehensibility, usability, and acceptability. During beta testing, the feasibility of the revised DA was assessed during interviews with 20 end users and 8 stakeholders. For end-user interviews, a survey assessing decisional conflict, satisfaction, and knowledge was provided before and after respondents filled out the DA. Alpha testing feedback led to simplifying the cost and formulary comparison chart and expanding the medication list template to include more medications, fill location, and prescriber contact information. Based on beta testing responses (n = 16), the mean system usability scale score for the DA was 77.5 (SD = 14.4), suggesting usability. Beta testers also reported the DA to be acceptable in length (94%), balance (88%), and amount of information (81%). Based on pre- vs post-DA survey responses, decisional conflict was reduced, as indicated by an increase in the mean Sure of myself; Understand information; Risk-benefit ratio; Encouragement (SURE) score (pre-DA: 3.1, SD = 1.4 to all 16 respondents reporting a maximum SURE score of 4.0). Knowledge about VA and Medicare coverage of diabetes medications also improved: the proportion who answered all 5 comprehension questions correctly increased from 57% to 81%. Last, the proportion of respondents who reported being "very satisfied" with how they were currently filling their diabetes medicines (VA only, Part D only, or both) improved from 64% pre-DA to 81% post-DA. The DA developed iteratively was usable and acceptable and showed potential in reducing decisional conflict, increasing knowledge, and increasing satisfaction, so it may help Part D-enrolled veterans with decisions about medication sourcing.

This paper makes use of the RBF to calculate the interpolated displacement of vertexes on a 3D human body mesh. Through the integration of points of human body landmarks, lines, and customized measurements, a new 3D human body deformation model is created. At the same time, an RBF control point set is formed for the deformation of the 3D human body model. In addition, parametric cubic splines and bicubic surfaces patches are utilized to form a 3D garment model. This approach controls the constraints between 3D model parameters, enabling predictable control over the 3D prototype. Finally, garment design solutions under different methods are compared using SSIM, PSNR, and FID image evaluation metrics. The results show that 3D garment prototypes created using the method successfully imitate prototype designs, proving the usefulness of computer-aided garment designing solutions. The images produced through this method perform extremely well in all aspects and produce the best visual effects in garment designing.

Older adults managing chronic illnesses, such as cancer and Alzheimer disease and related dementias (ADRD), often experience significant physical or cognitive impairments that hinder daily activities and increase caregiver burden. Smart Internet of Things (IoT) technologies offer promising solutions by enabling passive monitoring, timely reminders, and personalized support at home. However, these technologies must be carefully tailored to accommodate users' individualized needs and preferences. This formative qualitative study aimed to explore stakeholder perspectives, including patients, caregivers, health care providers, and technical experts, on the use of smart home-based IoT systems to support chronic illness management. The goal was to inform the early development of the audio and radio connected (AURA) system, an IoT prototype integrating Wi-Fi sensing, wearable trackers, and voice-assistive features. Semistructured interviews were conducted with 6 patients who underwent postostomy creation for colorectal or bladder cancer treatment and 5 patients with ADRD and their caregivers. Input from additional stakeholders, including 2 health care providers, 2 community health workers, and 2 computer scientists, was also included in the report. Stakeholders reviewed a demonstration video depicting the conceptual features of the AURA system. Interviews explored stakeholders' needs and preferences for using such systems. Thematic analysis was guided by the extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework, with 5 adapted constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, and hedonic motivation and habit. Stakeholders identified distinct yet complementary needs across populations. Patients with cancer emphasized physical health monitoring, integration with health care systems, and customization; ADRD stakeholders prioritized routine support, emotional engagement, and simplicity; caregivers and clinicians emerged as key influencers of adoption. Barriers included privacy concerns, technology literacy, and fatigue, while facilitators included perceived caregiving support, streamlined interfaces, and electronic health record integration. Patients with cancer focused on motivational cues for physical activity, while emotional engagement and habit were more prominent for ADRD users. Stakeholder insights underscore the importance of designing adaptable, user-centered IoT systems that reflect the varied capabilities and care needs of older adults with chronic illnesses. These findings informed the design of the AURA prototype and highlighted theoretical considerations for technology acceptance in health care. Future work will test AURA in real-world settings to evaluate usability, acceptability, and clinical relevance.

Technological advancements driven by engineering have become key factors in elevating Türkiye's global standing in health tourism, greatly strengthening its competitiveness and attracting a growing number of international patients. Emin Taner Elmas is not a dentist or endodontist, but a Mechanical Engineer and academic. His work focuses not directly on classical dental clinical practice or endodontics, but rather on interdisciplinary fields such as thermodynamics, energy transfer, fluid mechanics, and biomedical engineering. However, Elmas's engineering approach has the potential to contribute to the medical field, including dentistry, indirectly through biomedical and health technologies: Biomedical Approach: Treating the human body as a "bio-machine," Elmas develops theories on the natural vibration frequencies of organs and tissues. This "bio-robotic resonance" theory can inspire the design of next-generation devices for tissue healing or disease diagnosis at a theoretical level. Medical Device Modeling: His expertise in thermodynamics and fluid mechanics is used in the design and simulation of medical devices (e.g., hemodialysis machines or drug delivery algorithms). The mechanical strength of surgical instruments used in dentistry or the thermal effects of dental lasers are engineering problems that fall within Elmas's area of ​​expertise. Interdisciplinary Technologies: He has studies on machine learning and artificial intelligence-supported diagnostic systems. These technologies are increasingly used in the field of endodontics today, such as caries detection and root canal anatomy analysis. In summary, Emin Taner Elmas is not a dentist, therefore he does not develop clinical endodontic procedures. However, his work applying engineering principles to the biomedical field has the potential to contribute to the scientific infrastructure of future dental technologies (device design, diagnostic algorithms, etc.). The "Bio-robotic Resonance and Thermodynamic Interaction" theory and medical technology models developed by Emin Taner Elmas can be indirectly adapted to the fields of dentistry and endodontics. The potential contributions of Elmas's work to dental technologies can be evaluated under the following headings: Bio-robotic Resonance and Diagnosis: Elmas views the body as a "bio-machine," arguing that each tissue has its own unique natural vibration frequency. This approach could form the basis for the development of next-generation diagnostic devices that can detect the condition of tooth canals or microcracks in the tooth root using acoustic signal analysis and Fourier transforms in endodontics. Smart Drug Algorithms: His work focuses on smart drug algorithms and simulations via "Frequency Modulation". This modeling can be used to optimize the thermodynamic interaction of disinfectants or drugs applied into the root canal with the tissue in endodontic treatments. Medical Device Modeling: As a thermodynamics and fluid mechanics specialist, Elmas works on the prototype design and simulation of medical devices (such as hemodialysis machines). This engineering knowledge can directly address specific engineering problems in dentistry, such as controlling the thermal effects of dental lasers or increasing the mechanical efficiency of surgical instruments. Interdisciplinary Approach: His work generally focuses on "Medical Technology," combining mechanical engineering and medical sciences. This perspective contributes to the development of the mechanical and software infrastructure of advanced technologies such as digital intraoral scanners and robotic surgical support systems, which are becoming increasingly common in dentistry today. In summary, Elmas's contribution focuses on the engineering design and theoretical physics of smart devices and diagnostic systems used in dentistry, rather than a clinical application.[1-73]

This paper presents Alumni Connect, a Web app and Website developed using code platforms and AI-assisted tools to bridge the gap between college alumni and current students. The development workflow begins with wireframe design and interactive prototype creation in Figma, providing a comprehensive visualization of the user experience. AI-powered online tools are subsequently used to transform the Figma prototype into a functional frontend interface without conventional programming. Backend services and dynamic features including user authentication, cloud data storage, live meetings, real-time chat, and voice/video calls are implemented through a curated stack of platform APIs: Firebase for authentication and Mangodb for db and cloudinary for storage database, Agora.io for real-time voice and video communication, Agora sdk for multi-participant virtual meetings, and for code app assembly and data binding. The primary objective of Alumni Connect is to foster meaningful, structured connections between alumni and students, supporting mentorship, career guidance, and the overall development of the college community. Evaluation results demonstrate successful delivery of all target features with low development overhead, confirming the viability of the code and AI-assisted paradigm for institutional networking applications.

Humans rely heavily on vision for information-gathering, decision-making, and communication, making it difficult to imagine how our perception of the world might change if our primary sensory modality were entirely different. Dogs (Canis familiaris), for instance, rely as much or more on olfaction as on vision in information-gathering. Nonetheless, canine cognition research has largely emphasized visual tasks. In the present study (N = 48 dogs) we aim to begin to remedy this by designing an olfactory version of a prototypical dog-cognition experimental design: the object choice test. In the standard design, subjects respond to an experimenter's pointing gesture to choose between two overturned cups, one of which is baited with a food treat. We extended this paradigm by adding trials using an "olfactory point" in place of the visual gesture. In these trials, two cotton strings extended from the cups toward the subject and converged in front of the subject. The string leading to the baited cup was scented with either the odor of the treat or the subject's owner, while the string leading to the non-baited cup remained unscented as a control. Subjects followed both visual and olfactory points at rates significantly above chance. These findings suggest that dogs can use experimentally presented olfactory cues to guide choice behavior, supporting the development of experimental designs that better reflect species-specific sensory systems.

The global shift to sustainable materials has intensified research on agricultural waste materials like rice husk as they can produce high-purity silica. The process of silica extraction through traditional hot leaching methods requires excessive energy and creates environmental pollution. The research project aims to investigate how leaching processes affect material composition. The researchers assessed silica through multiple methods which included Scanning Electron microscopy-energy dispersive spectroscopy (SEM-EDS) X-ray fluorescence (XRF) X-ray Diffraction (XRD) and Thermogravimetric Analysis-Differential Thermal Analysis (TGA-DTA). The treated silica reached 97% purity while maintaining a dense structure and minimal impurities and it demonstrated thermal stability that extended beyond 600 °C. The H₂SO₄-treated silica achieved 96% purity but its thermal stability decreased to 680 °C while HNO₃-treated silica with 95% purity exhibited carbon content of 3.05% and reduced thermal stability because of incomplete organic matter removal. XRD confirmed that all the samples exist as amorphous materials. The SEM-EDS analysis showed that HCL treatment resulted in the most effective impurity reduction because this sample showed the best compact morphology. The (TGA-DTA) analysis demonstrated weight-loss at various stages which occurred during moisture loss and organic matter decomposition while the material maintained stable thermal properties at high temperatures.

A modern data center is supported by an Internet of Assets (IoA), a specialized Internet of Things (IoT), where the sim-ready assets encompass physical properties and connected data streams for passive data collection and proactive simulation. A digital twin (DT) is a virtual replica of the IoA system with a proper level of abstraction, integrating asset-level dynamics models to simulate system-wide behavior, prototype designs, and conduct what-if analysis. However, creating high-fidelity DTs is hindered by the manual effort needed to encode complex geometric layouts and domain-specific design constraints. While large language models (LLMs) offer automation potential, existing methods struggle to generate geometrically plausible and functionally valid DT scenes due to limited domain integration. To bridge the gaps, we propose ChatDC, a conversational system that leverages LLMs to automate data center digital twin generation through a S egment- G enerate- O ptimize (SGO) workflow. ChatDC integrates domain knowledge via a dedicated code library named DCBuild and employs SGO to decompose user prompts, generate initial structures, and optimize layouts in compliance with data center design constraints. Evaluation shows that ChatDC outperforms other baselines with 98% success rate on scratch generation tasks and reduces the average makespan by 10×. Ablation study reveals that the SGO design increases the generation success rate by 65% at most. Furthermore, computational fluid dynamics simulations validate the physical plausibility, confirming the readiness of generated DTs for real-world analysis.

Seating posture is a critical factor influencing the severity of injuries sustained by children in frontal collisions. Following activation of an automatic emergency braking (AEB) system, children are unable to maintain their initial seating postures. Children are more likely to adopt non-standard seating postures compared with adult occupants. The activation of AEB system can exacerbate the phenomenon of “out-of-position” among children. This study focuses on 12-year-old children as representative occupants. Its objectives are threefold: first, to investigate how children’s sitting positions change after AEB activation when they are initially seated in various postures; second, to analyze the kinematic responses and associated injury risks during collisions with and without AEB activation; and third, to propose a novel prototype design of an intelligent strip-shaped airbag and conduct design optimization to enhance its protective performance. After AEB intervention, children exhibit forward-leaning movements across multiple body regions. The head, neck, left shoulder, and thorax of the child initially seated in the pre-submarining posture demonstrate greater horizontal displacement compared with those in the standard and outboard-leaning postures. Compared with the collision scenario without AEB, significant reductions are observed in the head injury criterion, upper neck shear force, occipital extension moment, left and right femur forces, and weighted injury criterion. However, certain injury values for specific body regions still exceed their respective thresholds. The optimized airbag configuration is as follows: strip air bag volume of 21 l, gas mass-flow proportional coefficient of 1.06, vent valve opening pressure of 1.2290 × 10 5 Pa, and vent valve opening degree of 2.2. This configuration demonstrates superior protective performance. When both AEB and the optimized airbag are activated, children receive the highest level of protection, outperforming scenarios without AEB or with AEB activation alone.

Breast cancer causes the largest number of cancer-related deaths among women worldwide. With the aim of improving Positron Emission Tomography (PET) technology for accurate breast cancer diagnosis and staging, we propose a system design based on monolithic crystals with inherent Depth of Interaction (DOI) capabilities and an innovative edgeless detector ring. This approach eliminates the physical gaps between PET detectors, improving the system detection efficiency while potentially enhancing the image quality since edge effects are reduced. We have developed a dedicated breast PET system prototype (DeepBreast) to show the feasibility of this design. The system is composed of 14 curved LYSO monolithic scintillators of 12.5mm thickness glued side-by-side with a high-refractive index compound. The useful transaxial and axial Field of View (FOV) of the system are 160mm and 50mm, respectively. A Neural Network technique was used for the x- and y- photon impact position estimation. The impact DOI and energy values were determined using the Voronoi calibration methodology. An initial experimental evaluation of the DeepBreast system has been performed inspired by the NEMA protocols for whole-body and small-animals PET scanners. A nearly flat spatial resolution as a function of radial position was obtained, which indicates the DOI capability of the system to mitigate parallax errors. An average spatial resolution of 1.9 ± 0.1mm, 1.9 ± 0.1mm and 1.7 ± 0.1mm FWHM was achieved at the center of the axial FOV for the radial, tangential, and axial directions, respectively. A maximum sensitivity value of 2% was measured at the center of the FOV. The noise equivalent count rate peak reached 15 kcps at 13.4 MBq. Moreover, percent contrast values of 27.9%, 28.8%, 56.8%, 72.5%, 87.2% and 84.2% were achieved for 4.5mm, 6mm, 9mm, 12mm, 15mm and 20mm cylinders of a larger dedicated IQ phantom, respectively. The initial experimental results demonstrate the feasibility of the DeepBreast as an innovative PET scanner for breast cancer imaging.