Search Results (56)

The environment of today’s companies is marked by increasing dynamism. Rapid technological developments, strong innovation impulses, and continual market entry of new competitors create volatile conditions that make the delivery of valuable products challenging. Long-term corporate success therefore depends on offering a product portfolio consistently aligned with evolving market needs. Customers expect products that show continuous improvements in performance and functionality over time, making systematic product upgrading a key success factor. Release planning addresses this need by enabling continuous product evolution through planned product upgrades. It focuses on selecting and combining functional units for structured publication within releases. This proactive management of product value offers substantial potential but also demands comprehensive know-how, particularly given rising product complexity and the interplay of multiple technologies. The objective of this work is to develop a methodology that supports effective planning of product upgrades. The method assists in the product-specific selection of release types and the derivation of suitable release strategies. It yields release units defined by product structure and provides recommendations for appropriate release strategies. The methodology is demonstrated through its application to an electric vehicle, illustrating its practical relevance for software-intensive products. Full article

Background: Rehabilitation technologies, including assistive devices, adaptive software, and robotic systems, are increasingly integral to contemporary rehabilitation practice. Yet, ensuring that training in their use is inclusive and accessible remains a critical challenge. Methods: This study reports findings from patient and public involvement (PPI) activities conducted by the National Institute for Health and Care Research (NIHR) HealthTech Research Centre in Rehabilitation. Fifteen contributors participated, comprising rehabilitation professionals and educators, individuals with lived experience of serious illness, injury, or disability requiring rehabilitation, and technology innovators. The purpose of these activities was to identify the factors necessary to ensure that training in rehabilitation technologies is equitable for people with sensory, cognitive, and physical impairments. Findings: Contributors highlighted a series of priority domains that together capture the breadth of challenges and opportunities in this area. These included the need to address physical, sensory, and cognitive accessibility; to foster participation, motivation, and engagement; to strengthen instructional design and delivery; to ensure technological accessibility and integration; to enhance staff training and competence; and to embed participant-centred and policy approaches. Contributions in these domains were synthesised into thematic categories that provide a structured understanding of the training requirements of rehabilitation technology recipients. Evaluation: The PPI process was evaluated using the Guidance for Reporting Involvement of Patients and the Public (GRIPP2) Short Form, supplemented by an evaluation survey. This dual approach ensured that the contributions were systematically documented and critically appraised. Implications: Guided by implementation science, the principal output of this work was a co-created stakeholder engagement strategy, structured using the Implementation STakeholder Engagement Model (I-STEM). This plan will serve as a foundation for future research exploring the education and training needs of diverse stakeholder groups, thereby contributing to the development of more inclusive and effective rehabilitation technology training practices. Full article

Background: nowadays, traditional delivery options are challenging to the urban last-mile logistics and sustainability goals. The purpose of this study is to investigate the practical factors that drive frequent e-shoppers to actively switch their intention from conventional delivery options to utilizing smart lockers. Methods: the hypothetical framework tested integrating constructs from the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), and supplementary constructs such as privacy and convenience. Data were collected via a structured online questionnaire from 513 respondents in major Egyptian cities, including Alexandria and Cairo. The framework was tested using Structural Equation Modeling (SEM) via SmartPLS 4.0 software to assess the relationship between constructs and switching intention. Results: the analysis confirms that switching intention to use smart lockers is positively driven by Perceived Usefulness, Perceived Ease of Use, Convenience, Privacy, and Perceived Behavioral Control. Notably, a positive attitude towards smart lockers was found to have a non-significant effect on the intention to switch in the Egyptian context. Conclusions: this research contributes to addressing the gap in the extant literature by focusing on analyzing the unique contextual determinants in the emerging last-mile logistics within a developing market context. Full article

Advances in digital dental technologies have transformed implant therapy from analog, stepwise processes into advanced, data-driven workflows spanning diagnosis, planning, surgery, and prosthetic delivery. Contemporary digital implantology integrates multiple techniques, tools, and multimodal datasets into comprehensive diagnostic models and treatment workflows, enhancing implant placement accuracy, procedural efficiency, patient experience, and interdisciplinary coordination. However, integration remains constrained by fragmented datasets, diverse software platforms, and parallel surgical and prosthetic streams. These interfaces often require manual user intervention to convert, process, and align data, thereby increasing the risk of data loss, artifact generation, misalignment, and error accumulation, which may impact implant and prosthetic restorative outcomes. Similarly, implant and prosthetic planning steps continue to rely on subjective, non-standardized user input, requiring advanced experience and training. This narrative review synthesizes current evidence and technical developments in digital implant dentistry based on literature searches in PubMed, Scopus, and Web of Science, with emphasis on publications from 2010 onward, prioritizing systematic reviews, randomized clinical trials, and technical reports focusing on key technological innovations. It presents the current state of the art in digital implantology and identifies major workflow interfaces that constrain seamless, end-to-end integration. This part I summarizes contemporary tools and approaches in digital implant technology. In contrast, Part II of this series will address the emerging roles of artificial intelligence and robotics in overcoming these limitations and advancing toward fully integrated digital implant prosthodontic workflows. Overall, current digital implant workflows are clinically reliable and are equivalent to, or often superior to, conventional approaches in terms of efficiency and accuracy. Nevertheless, their full potential remains limited by persistent software, data, and process interface barriers. Full article

Background: Integrating deep regional hyperthermia (HT) with neoadjuvant chemoradiotherapy (CRT) may enhance treatment efficacy in locally advanced rectal cancer (LARC), yet feasibility and tolerance data remain scarce for both short-course (SCRT) and long-course (LCRT) radiotherapy (RT) regimens. Methods: In this single-center prospective observational study, 67 LARC patients received neoadjuvant RT and chemotherapy (CT) combined with deep radiative HT using a phased-array system (ALBA 4D). Patients treated with SCRT (5 × 5 Gy) were prescribed two HT sessions; those treated with LCRT (25 × 2 Gy) were prescribed ten. HT planning was guided by dedicated software, and real-time thermometry ensured precise thermal delivery. Feasibility was defined as completion of ≥50% of prescribed sessions. Tolerance and toxicity were assessed with standardized clinical scales (QMHT, UMC, CTCAE v4.03). Results: HT was feasible in both groups: 100% of SCRT and 63.6% of LCRT patients completed ≥50% of prescribed sessions. In total, 243 sessions were delivered. Most symptoms were mild and transient, predominantly localized pain. No grade ≥3 HT-related toxicities occurred. All scheduled RT and surgery proceeded without delay. Median T50 was 40.3 °C (SCRT) and 40.4 °C (LCRT); the median RT-to-HT interval was 42 min in both groups. Conclusion: This first Spanish experience shows that deep radiative HT can be seamlessly integrated into both SCRT and LCRT neoadjuvant protocols for rectal cancer. High adherence, favorable tolerance, and reliable thermal control support clinical implementation. Any between-schedule observations are descriptive only; no formal comparative testing was performed. The study was not designed or powered to establish comparative effectiveness between SCRT and LCRT, and the sample size was insufficient to detect rare HT-specific adverse events. Full article

The purpose of this research is to systematically map and analyze the use of AI technologies in project management, identifying themes, research gaps, and practical implications. This study conducts a systematic literature review (SLR) that combines bibliometric analysis with qualitative content evaluation to explore the present landscape of AI in project management. The search covered literature published until November 2024, ensuring inclusion of the most recent developments. Studies were included if they examined AI methods applied to project management contexts and were published in peer-reviewed English journals as articles, review articles, or early access publications; studies unrelated to project management or lacking methodological clarity were excluded. It follows a structured coding protocol informed by inductive and deductive reasoning, using NVivo (version 12) and Biblioshiny (version 4.3.0) software. From the entire set of 1064 records retrieved from Scopus and Web of Science, 27 publications met the final inclusion criteria for qualitative synthesis. Bibliometric clusters were derived from the entire set of 885 screened records, while thematic coding was applied to the 27 included studies. This review highlights the use of Artificial Neural Networks (ANN), Case-Based Reasoning (CBR), Digital Twins (DTs), and Large Language Models (LLMs) as central to recent progress. Bibliometric mapping identified several major thematic clusters. For this study, we chose those that show a clear link between artificial intelligence (AI) and project management (PM), such as expert systems, intelligent systems, and optimization algorithms. These clusters highlight the increasing influence of AI in improving project planning, decision-making, and resource management. Further studies investigate generative AI and the convergence of AI with blockchain and Internet of Things (IoT) systems, suggesting changes in project delivery approaches. Although adoption is increasing, key implementation issues persist. These include limited empirical evidence, inadequate attention to later project stages, and concerns about data quality, transparency, and workforce adaptation. This review improves understanding of AI’s role in project contexts and outlines areas for further research. For practitioners, the findings emphasize AI’s ability in cost prediction, scheduling, and risk assessment, while also emphasizing the importance of strong data governance and workforce training. This review is limited to English-language, peer-reviewed research indexed in Scopus and Web of Science, potentially excluding relevant grey literature or non-English contributions. This review was not registered and received no external funding. Full article

This study uses the CREATE^TM-AV/Kestrel simulation software to model a towed jumper scenario using standard aircraft settings to quantify paratrooper stability and risk of contact during static line airborne operations. The focus areas of this study include a review of the technical build-up, which includes aircraft, paratrooper and static line modeling, plus preliminary functional checkouts executed to verify simulation performance. This research and simulation development effort is driven by the need to meet the analysis demands required to support the US Army Personnel Airdrop with static line length studies and the North Atlantic Treaty Organization (NATO) Joint Airdrop Capability Syndicate (JACS) with airdrop interoperability assessments. Each project requires the use of various aircraft types, static line lengths and exit procedures. To help meet this need and establish a baseline proof of concept (POC) simulation, simulation setups were developed for a towed jumper from both the C-130J and C-17 using a 20-ft static line to support US Army Personnel Airdrop efforts. Concurrently, the JACS is requesting analysis to support interoperability testing to help qualify the T-11 parachute from an Airbus A400M Atlas aircraft, operated by NATO nations. Due to the lack of an available A400M geometry, the C-17 was used to demonstrate the POC, and plans to substitute the geometry are in order when it becomes available. The results of a nominal Computational Fluid Dynamics (CFD) simulation run using a C-17 and C-130J will be reviewed with a sample of the output to help characterize performance differences for the aircraft settings selected. The US Army Combat Capabilities Development Command Soldier Center (DEVCOM-SC) Aerial Delivery Division (ADD) has partnered with the US Air Force Academy (USAFA) High Performance Computing Research Center (HPCRC) to enable Modeling and Simulation (M&S) capabilities that support the Warfighter and NATO airdrop interoperability efforts. Full article

Background and Aim: In 2021, healthcare expenditure in Italy represented 7.3% of the national gross domestic product, with approximately 80% attributed to the management of chronic diseases—an increasing burden associated with population aging. Population stratification tools have emerged as critical instruments for the efficient allocation of healthcare resources, particularly for high-need, high-cost individuals. This systematic review aimed to identify, classify, and evaluate existing population stratification tools based on their characteristics, validation status, and practical applications. Methods: A systematic literature review was conducted in accordance with the PRISMA 2020 guidelines to examine adult population stratification models grounded in healthcare needs. The review encompassed studies retrieved from major scientific databases and included both national and international implementations. Results: The initial search yielded 140,111 records, from which 17 distinct stratification tools were identified. Of these, nine had undergone validation through peer-reviewed studies. Within the Italian context, only six tools were in active use—three of which were developed as region-specific algorithms, while the remaining three employed internationally established software platforms. Conclusions: Population stratification tools provide a robust framework for assessing both clinical complexity and resource utilization, thereby facilitating the design of integrated care pathways and evidence-based policy decisions. In the context of proactive and personalized healthcare delivery, such tools play a pivotal role in enhancing system efficiency, informing strategic planning, and promoting equitable access to care. Full article

Common practice in agile delivery planning is based on user requirements-related artifacts. However, an aspect of business process alignment to software product functions comes into focus in the phase of inception of enterprise-aware disciplined agile software projects. This research proposes a method for mapping business process model elements to sets of semantically coupled and prioritized software functions to obtain ordered software product backlog, i.e., work items list. These software functions are derived from primitive business processes and software functional patterns. The mapping table enables assignment of primitive business processes to categorized software functions. Derived and prioritized software functions are related to a work item list pattern according to selected technology implementation. This way, a prioritized work items list is formulated, which enables development iteration planning. This method could be useful in software functional design alternatives comparison, change management, multi-project integration of software modules to support business processes in information systems, etc. Feasibility of the proposed method has been demonstrated with a case study, related to the development of a billing and reporting software utilized in a private hospital. This case study shows usability of the proposed method in the case of two related development projects that enable software functionality enhancement. Full article

Background: Quality assurance (QA) programs are designed to improve the quality and safety of radiation treatments, including patient-specific QA (PSQA). The objective of this study was to investigate the conditions in which pretreatment PSQA is performed, to evaluate the root cause of the implementation of more complex techniques, and to identify areas for potential improvement. Materials/Methods: The Octavius 4D (O4D) system accuracy was evaluated using an O4D homogeneous phantom for different field sizes. Tests of the system response to dose linearity, field sizes, and PDD differences were performed against calculated doses for a 6 MV photon beam. The pretreatment verification of 40 VMAT plans was performed using the PTW VeriSoft software (version 8.0.1) for local and global 3D gamma analysis. The reconstructed 3D dose was compared to the calculated dose using 2%/2 mm and 3%/3 mm, 20% of the low-dose threshold, and 95% of the gamma passing rate (%GP) tolerance level. The sensitivity of the O4D system in detecting VMAT delivery and setup errors has been investigated by measuring the variation in %GP values before and after the simulated errors. Results: The O4D system reported good agreement for linearity, field size, and PDD differences with TPS dose, being within ±2% tolerance. The output factors were consistent between the ionization chamber and the O4D detector down to a 4 × 4 cm² field size with a maximum deviation less than 1%. The introduction of deliberate errors caused a decrease in %GP values. In most scenarios, the %GP value of the simulated errors was detected with 2%/2 mm. Conclusion: The results indicate that the O4D system is sensitive enough to detect delivery and setup errors with the restrictive global criterion of 2%/2 mm for routine pretreatment verification. Full article

Background and Objectives: To assess pregnancy and delivery complications in obese women with gestational diabetes mellitus (GDM) and neonatal weight and condition after birth. Materials and Methods: A retrospective tertiary referral centre study included all cases of GDM in the Department of Obstetrics and Gynaecology of the Lithuanian University of Health Sciences (LUHS) Birth Registry from 1 January 2019 to 31 December 2019. We included 583 women with GDM and singleton pregnancies. Women were divided into two groups according to their pre-pregnancy weight: 202 were obese (BMI ≥ 30 kg/m²), and 381 were weight (BMI 18.5–24.9 kg/m²). Survey data were analysed using IBM SPSS Statistics 26.0 and MS Excel 2016 software. A value of p < 0.05 was considered significant. Results: Fasting glycaemia was significantly higher in obese women with GDM than in normal-weight women with GDM (p < 0.001). In addition, fasting glycaemia was higher in obese women with GDM requiring insulin correction than in normal-weight women (p = 0.006). OGTT 0-min glycaemia was higher in obese than in non-obese women with gestational diabetes (p < 0.001). Women with pre-pregnancy obesity had a higher incidence of primary hypertension (p < 0.001), hypertensive disorders in pregnancy (p < 0.001), gestational cholestasis (p = 0.002), polyhydramnios (p < 0.001), induced labour (p < 0.001), and caesarean section (p = 0.015), with emergency caesarean sections being more frequent than planned caesarean sections (p = 0.011) compared to normal-weight women with GDM. Labour dystocia (p = 0.003) and foetal distress (p = 0,019) were more common during labour in obese women. Neonates of these women more often had macrosomia (p < 0.001) and lower Apgar scores at 1 min (p = 0.024) and at 5 min (p = 0.024) compared to neonates of normal-weight women. Conclusions: Obese women with GDM experience more pregnancy complications than normal-weight women with GDM. Full article

Full-arch zirconia restorations on implants have gained popularity due to zirconia’s strength and aesthetics, yet they are still associated with challenges like structural fractures, peri-implant complications, and design misfits. Advances in CAD/CAM and digital workflows offer potential improvements, but a technique that consistently addresses these issues in fixed, full-arch, implant-supported prostheses is needed. This novel technique integrates a facially and prosthetically driven treatment approach, which is divided into three phases: data acquisition, restoration design, and manufacturing/delivery. Digital tools, including intraoral scanning and photogrammetry, facilitate accurate implant positioning, while 3D design software enables functional and aesthetic validation before final milling. A dual software approach is used to reverse engineer a titanium bar from the final restoration design, ensuring a superior outcome to other protocols. The restoration incorporates a zirconia–titanium hybrid structure, optimizing strength, flexibility, and weight. The proposed workflow enhances restoration precision and predictability through a prosthetically driven treatment plan, by ensuring passivity and aligning with biological and mechanical principles to promote long-term stability. By starting with the proposed restoration design and reverse engineering the bar, while also allowing for flexibility in material and component choices, this technique accommodates both patient needs and financial considerations. This approach demonstrates potential for improving patient outcomes in full-arch implant restorations by minimizing complications associated with traditional methods. Further research is recommended to validate the technique’s efficacy and broaden its clinical applications. Full article

Planning and managing water resource systems with multipurpose surface reservoirs demand the careful consideration of several critical factors, including system longevity, multiple uses, phased development, substantial financial investments, complex management requirements, uncertainties in predicting future hydrological inputs, and the estimation of economic parameters. Despite the importance of these factors, recent research and software development efforts for water resource system planning and management have predominantly focused on hydrological models. Economic models that describe the phased growth of such systems are often either absent or not fully integrated into decision-making software. Well-established network-based decision support tools, such as MODSIM, AcquaNet, and SIMYLD-II, provide robust hydrological modeling but lack explicit economic sub-models to address the evolving nature of water resource systems over time. This paper introduces a novel approach that combines optimization and simulation techniques to evaluate the multi-year performance of a growing water resource system. By conducting multiple model runs for each phase of system development—each characterized by unique topologies, demand patterns, hydrological conditions, and water delivery preferences—economic analyses are suggested to complete the global evaluation of the system. This comprehensive approach provides valuable insights into long-term planning horizons. Research in this direction continues to evolve, aiming to bridge the gap between hydrological and economic modeling in decision support frameworks. To illustrate the proposed methodology, a test example is presented, showcasing the implementation of the economic calculations and computer routines described in the paper. This example serves as a foundational step toward more comprehensive tools for sustainable and economically viable water resource system planning and management. Full article

Recent advancements in Unmanned Aerial Vehicle (UAV) technology have made them effective platforms for data capture in applications like environmental monitoring. UAVs, acting as mobile data ferries, can significantly improve ground network performance by involving ground network representatives in data collection. These representatives communicate opportunistically with accessible UAVs. Emerging technologies such as Software Defined Wireless Sensor Networks (SDWSN), wherein the role/function of sensor nodes is defined via software, can offer a flexible operation for UAV data-gathering approaches. In this paper, we introduce the “UAV Fuzzy Travel Path”, a novel approach that utilizes Deep Reinforcement Learning (DRL) algorithms, which is a subfield of machine learning, for optimal UAV trajectory planning. The approach also involves the integration between UAV and SDWSN wherein nodes acting as gateways (GWs) receive data from the flexibly formulated group members via software definition. A UAV is then dispatched to capture data from GWs along a planned trajectory within a fuzzy span. Our dual objectives are to minimize the total energy consumption of the UAV system during each data collection round and to enhance the communication bit rate on the UAV-Ground connectivity. We formulate this problem as a constrained combinatorial optimization problem, jointly planning the UAV path with improved communication performance. To tackle the NP-hard nature of this problem, we propose a novel DRL technique based on Deep Q-Learning. By learning from UAV path policy experiences, our approach efficiently reduces energy consumption while maximizing packet delivery. Full article

A 3D range-modulator (RM), optimized for a single energy and a specific target shape, is a promising and viable solution for the ultra-fast dose delivery in particle therapy. The aim of this work was to investigate the impact of potential beam and modulator misalignments on the dose distribution. Moreover, the FLUKA Monte Carlo model, capable of simulating 3D RMs, was adjusted and validated for the 250 MeV single-energy proton irradiation from a Varian ProBeam system. A 3D RM was designed for a cube target shape rotated 45° around two axes using a Varian-internal research version of the Eclipse treatment planning software, and the resulting dose distribution was simulated in a water phantom. Deviations from the ideal alignment were introduced, and the dose distributions from the modified simulations were compared to the original unmodified one. Finally, the FLUKA model and the workflow were validated with base-line data measurements and dose measurements of the manufactured modulator prototype at the HollandPTC facility in Delft. The adjusted FLUKA model, optimized particularly in the scope of a single-energy FLASH irradiation with a PMMA pre-absorber, demonstrated very good agreement with the measured dose distribution resulting from the 3D RM. Dose deviations resulting from modulator-beam axis misalignments depend on the specific 3D RM and its shape, pin aspect ratio, rotation angle, rotation point, etc. A minor modulator shift was found to be more relevant for the distal dose distribution than for the spread-out Bragg Peak (SOBP) homogeneity. On the other hand, a modulator tilt (rotation away from the beam axis) substantially affected not only the depth dose profile, transforming a flat SOBP into a broad, Gaussian-like distribution with increasing rotation angle, but also shifted the lateral dose distribution considerably. This work strives to increase awareness and highlight potential pitfalls as the 3D RM method progresses from a purely research concept to pre-clinical studies and human trials. Ensuring that gantry rotation and the combined weight of RM, PMMA, and aperture do not introduce alignment issues is critical. Given all the other range and positioning uncertainties, etc., not related to the modulator, the RM must be aligned with an accuracy below 1° in order to preserve a clinically acceptable total uncertainty budget. Careful consideration of critical parameters like the pin aspect ratio and possibly a novel robust modulator geometry optimization are potential additional strategies to mitigate the impact of positioning on the resulting dose. Finally, even the rotated cube 3D modulator with high aspect ratio pin structures (~80 mm height to 3 mm pin base width) was found to be relatively robust against a slight misalignment of 0.5° rotation or a 1.5 mm shift in one dimension perpendicular to the beam axis. Given a reliable positioning and QA concept, the additional uncertainties introduced by the 3D RM can be successfully managed adopting the concept into the clinical routine. Full article