Exploratory Phase Research Articles

Enhancing parameter identification for proton exchange membrane fuel cell using modified manta ray foraging optimization

In this paper, an accurate model of proton exchange membrane fuel cell (PEMFC) for optimal identification of PEMFC parameters has been developed. The optimization methodology is based on the modified version of Manta Ray Foraging Optimization (MMRFO) technique for minimizing the sum of squared errors (SSE) between the Experimentally measured stack voltage and the estimated voltage produced by the optimized model. In the modified methodology, the sine-cosine method has been utilized to enhance the global searching capability in the exploration phase and the local searching capability in the exploitation phase of the MRFO algorithm. In order to validate the effectiveness of the suggested methodology, four different case studies comprising standard benchmark 250 W PEMFC, BCS-500 W PEMFC, SR-12 500 W FC, and 1 kW Temasek stacks were utilized, and the attainments have been compared with the measured polarization characteristics. The attainments have been intensively compared with several metaheuristic algorithms (MA) including Tree growth Algorithm (TGA), Grey wolf optimizer (GWO), Whale optimization algorithm (WOA), Salp swarm algorithm (SSA), and original Manta Ray Foraging Optimization (MRFO), to confirm the superiority of the MMRFO against the compared techniques. The obtained results give a satisfactory agreement between the MMRFO-based model and the experimentally measured data. Finally, the achievements confirmed the effectiveness of the MMRFO over the basic MRFO algorithm and other novel metaheuristic algorithms in identifying PEMFC parameters.

Design, Control, and Clinical Applications of Magnetic Actuation Systems: Challenges and Opportunities

The enhancement of medical services relies significantly on engineering research. Magnetic actuation systems (MASs) are designed to be safe for biomedical applications and offer long‐distance and dynamic control capabilities, rendering them highly favorable for clinical applications. The integration of MASs with sophisticated perception and positioning methods enhances their suitability for clinical applications. Achieving this goal hinges on the development of MASs and their control methods; thus, a comprehensive review of current technical and scientific issues is indispensable for addressing the related challenges. This review encompasses both the classical and state‐of‐the‐art research, providing readers with a thorough understanding of the intricacies involved in MAS design, system control methods, clinical applications, and guidance toward the next phase of exploration. In addition, a detailed illustration of the underlying principles of the magnetic actuation method is provided, which helps delineate the design principles. A detailed analysis of potential clinical applications in hard‐to‐reach environments inside the human body provides guidance for interdisciplinary researchers. Challenges and opportunities associated with MAS design and application are summarized to inspire researchers and stimulate innovative explorations. Finally, this review concludes that sustained research in this field has the potential to yield groundbreaking advancements capable of revolutionizing modern medicine.

Exploratory phase I study of HF1K16 for the treatment of patients with refractory/recurrent advanced glioma.

TPS32 Background: Glioblastoma (GBM) is the most common primary and aggressive malignant brain tumor in adults. Almost all patients with glioblastoma eventually have disease relapse and the median overall survival (OS) remains at only 30 weeks for those recurrent GBM patients. However, very few therapies had been approved over the past two decades, emphasizing the need for novel treatments. It is now wildly acknowledged that the immune microenvironment in brain offers adequate opportunities to implement immunotherapy for the treatment. Since most recurrent tumors had previously been exposed to the genotoxic stress of irradiation and/or chemotherapy, it is predicted to resulted in a higher mutational load and more immunogenic. However, The limited success of T cell immunomodulatory approach for advanced glioma has prompted a deeper understanding of the brain tumor and the immune microenvironment. GBM tumors are well-appreciated to be a immunosuppressive and a hallmark of GBM immunosuppression is the appearance of circulating myeloid-derived suppressor cells (MDSCs) at higher levels. Our preliminary data suggests that HF1K16, a liposome ATRA (all trans retinoic acid) suspension, is capable of relieving immune suppression induced by MDSCs. Further, we recently completed a phase I dose escalation study which showed that HF1K16 is safe, well-tolerated with preliminary efficacy in brain tumor. Thus, we hypothesized that HF1K16 may reverse GBM-associated immune suppression and leading to an increase in TILs and improved survival. Methods: This is an advanced glioma-specific expansion arm for adult patients with prior confirmed brain tumor and failed standard treatment. We plan to enroll 20~30 evaluable patients. Key eligibility criteria include candidates age ≥ 18 years, Karnofsky performance status ≥ 60 with and at least one measurable lesion. The primary endpoint is determination of overall response rate (ORR), duration of response (DOR), disease control rate (DCR) and progression-free survival (PFS) according to RANO criteria. Secondary endpoints include longitudinal assessment of the peripheral immune response by changes in MDSC and T cell subsets and numbers. HF1K16 infusions were administered in 21-day cycles (q.o.d days 1-14). Prior to and during treatment, peripheral blood mononuclear cells were collected and analyzed with flow cytometry to monitor the changes in myeloid cell phenotype and T cell composition. The recruitment of patients is anticipated completed in 2024. Clinical trial information: NCT05388487 .

Low-Cost Automated Vegetable Garden: Integrating Educational Robotics in High School

Objective: The objective of this study is to investigate the integration of Educational Robotics (ER) into high school curricula through the development of the Automated Vegetable Garden project, aiming to enhance student learning and support sustainable agricultural practices among small local producers. Theoretical Framework: This study is based on the theories of STEAM education, problem-based learning (PBL) and the principles of Design Thinking. These frameworks provide a foundation for inserting Educational Robotics into real-world interdisciplinary problems and enhancing individual and collaborative learning. Method: The methodology adopted for this research comprises a Design Thinking approach divided into phases of exploration, ideation and prototyping. Participants include high school students and small, local producers. Materials used include the ESP8266 microcontroller and low-cost sensors, and to the use of Google Sheets for data collection. Data was collected through field testing. Results and Discussion: The results revealed the integration of Educational Robotics through the Automated Garden project, with two educational interventions being proposed: project-based, with the creation of the prototype, and another based on Storytelling. Research Implications: Practical implications of this research include the development of improved educational practices in ER, promoting students' skills in digital technologies and problem-solving. The project also promotes sustainable agricultural practices by providing smallholder farmers with actionable data. Originality/Value: This study contributes to the literature by demonstrating an approach to integrating Educational Robotics into high school curricula through practical real-world applications. The relevance and value of this research is evidenced by its potential to improve educational outcomes and support sustainable agriculture in local communities.

An improved Genghis Khan optimizer based on enhanced solution quality strategy for global optimization and feature selection problems

Feature selection (FS) is the activity of defining the most contributing feature subset among all used features to improve the superiority of datasets with a large number of dimensions by selecting significant features and eliminating redundant and irrelevant ones. Therefore, this process can be seen as an optimization process. The primary goals of feature selection are to decrease the number of dimensions and enhance classification accuracy in many domains, such as text classification, large-scale data analysis, and pattern recognition. Several metaheuristics, such as the Genghis Khan Shark Optimizer Algorithm (GKSO), can assist in optimizing the FS issue. However, these methods tend to converge towards local solutions with a low convergence rate. In order to address this issue in GKSO, a more refined version called I-GKSO is implemented. The I-GKSO suggested introducing a new approach to modify solutions that have low fitness values. In addition, it employs the Enhanced Solution Quality (ESQ) strategy to enhance the exploration phase. It utilizes Quasi-opposite-based learning (QOBL) to enhance the best solution obtained and, consequently, the entire population. The algorithm presented aims to solve the FS problem and has been assessed using benchmark optimization problems from the CEC’2017 and CEC’2022. To assess the efficacy of the I-GKSO, it has been subjected to comparisons with multiple different algorithms. The trials conducted using FS datasets yield a quantitative consideration of the I-GKSO's capacity to attain the most optimal subset of features. Furthermore, Wilcoxon and Friedman's non-parametric tests were accomplished to support the performance of the proposed method.

Dynamics of corporate social responsibility (CSR) during mineral exploration: Evidence from Eastern Region, Ghana

How are decisions around Corporate Social Responsibility (CSR) arrived at in the mining sector, particularly in Ghana? To date, the literature on mining and development in the region has mostly analysed individual interventions made in the name of CSR and critiqued the case for embracing it. These assessments, however, very crucially overlook exploration, an essential phase of the mine lifecycle, during which inaugural dialogues are initiated with local communities that ultimately have a bearing on CSR strategy over the long term. This paper examines the dynamics of CSR at the exploration phase of mining projects, surveys local communities’ perspectives on the subject, and assesses the actions of international mineral exploration companies. It does so using semi-structured interviews and focus group discussions with policymakers, exploration company officials, and inhabitants of and leaders in mining communities in the Eastern Region of Ghana. The findings suggest that while mineral exploration companies use CSR as platform to showcase their commitments to social and environmental standards, they have made little efforts to understand the socio-cultural, economic and political dynamics of their host communities. It was also revealed that host communities are not afforded the opportunity to provide inputs into decisions on how CSR should be operationalised. The paper calls for a revision of policies and regulations governing mining in Ghana by making CSR a key requirement for mineral exploration companies seeking to undertake exploration or prospecting activities in the country.

Silk-Derived Protein-4 Versus Vehicle Control in Treating Patients With Moderate to Severe Dry Eye Disease: A Randomized Clinical Trial

In this study the safety and efficacy of silk-derived protein 4 (SDP-4), also known as amlisimod, eye drops against a vehicle control formulation in patients with moderate to severe dry eye disease (DED) was assessed. SDP-4 is a novel, naturally derived, anti-inflammatory wetting agent that enhances coating on the ocular surface. Exploratory Phase 2, 12- and 8-week, serial cohort, multicenter, double-masked, randomized, vehicle-controlled study. In the first cohort (N = 305), patients were randomized 1:1:1:1 to SDP-4 (0.1%, 1%, 3% wt./wt.) or vehicle control and dosed 2 times per day (BID), while in the second cohort patients were randomized 1:1 with 1% wt./wt. SDP-4, the best performing formulation from the first cohort, or vehicle control BID (N = 151). Diagnosed DED patients were treated in the United States between April 2019 and May 2021. The first cohort of subjects had moderate to severe baseline symptoms, while the second cohort had moderate baseline symptoms to study the impact of baseline symptoms on SDP-4 performance. Key sign and symptom end points were mean change from baseline in TBUT and total SANDE score (0-100 visual analog scale) throughout the study. SDP-4 (1%) significantly increased TBUT vs the vehicle control (P < .05) at days 28 and 56 in the first cohort, and patient symptomatology from baseline was reduced by 46% based on subject reported SANDE VAS scores at day 84. Patients with more severe baseline DED symptoms experienced a significantly greater amount of relief than when compared to patients with moderate DED (P < .05). All treatment groups were well tolerated with a 2.6% total discontinuation rate. To the best of our knowledge, this was the first-in-human use of SDP-4 in a clinical trial. SDP-4 is a first-in-class protein ingredient that offers a safe and multi-modal treatment approach for alleviating severe DED symptoms within a novel formulation.

An improved Harris hawks optimizer with enhanced logarithmic spiral and dynamic factor and its application for predicting molten iron temperature in the blast furnace

AbstractIn response to the problem of poor search performance and difficulty in escaping from the local optimum in the Harris hawks optimizer, an improved Harris hawks optimizer with enhanced logarithmic spiral and dynamic factor (IHHO‐ELSDF) is proposed in this paper. The enhanced logarithmic spiral mechanism is adopted in the exploration phase, and its main feature is the use of an improved opposite‐learning hybrid logarithmic spiral mechanism to search for more promising regions. The dynamic factor is used to replace the escaping energy to improve the global search capability of the algorithm, and it can better balance exploration and exploitation. In addition, a random distribution strategy is proposed for the exploitation phase to avoid falling into the local optimum. Based on 23 classical test functions, the influence of the distribution probability, the three improved mechanisms, and the exploration–exploitation ratio in IHHO‐ELSDF are analyzed. Subsequently, IHHO‐ELSDF is subjected to a comparative analysis with 17 algorithms on the IEEE CEC2022 benchmark suite. These tests show that IHHO‐ELSDF outperforms most competitors in numerical optimization. Furthermore, to assess its applicability in real‐world problems, IHHO‐ELSDF is employed to optimize parameters in the wavelet neural network used for molten iron temperature prediction. The simulation results based on real production data show that the proposed prediction model achieves a high prediction precision with , , and .

A single-center pilot study on the efficacy and safety of acetic acid–enhanced magnifying endoscopy with narrow-band imaging for diagnosis of colorectal epithelial neoplasms

Background and aimsWe investigated the ideal acetic acid (AA) concentration for AA-enhanced, narrow-band imaging (NBI) magnifying endoscopy (ANBI-ME) in the diagnosis of superficial colorectal neoplasms and evaluated its clinical efficacy. MethodsDuring the exploratory phase, we investigated four concentrations (1.5%, 2.25%, 3.0%, and 4.5%) in rotation by performing ANBI-ME on 50 superficial colorectal neoplasms at each concentration. A favorable AA concentration was determined by evaluating the diagnostic accuracy, AA whitening duration (AD), peristalsis, and bleeding following endoscopic resection. In the validation phase, we assessed inter-observer agreements for ANBI-ME with the determined AA concentration and inter-methodological agreements between that and subsequently conducted crystal violet-stained magnifying endoscopy (CV-ME) with the exploratory set and 98 additional patients. ResultsThe diagnostic accuracies were 89.3% (42/47) for 1.5% AA, 92.0% (46/50) for 2.25%, 96.8% (61/63) for 3.0%, and 97.8% (46/47) for 4.5% with no significant difference (p = 0.26). A significant positive correlation was observed between the AA concentrations and ADs (p < 0.001). No significant differences in hyperperistalsis or post-resection bleeding were observed. The optimal AA concentration was determined to be 4.5%. In the validation analysis, the accuracy rates were 72.4% (105/145) using AMBI-ME and 68.3% (99/145) using CV-ME (p = 0.43). Strong agreements were noted between two observers (κ, 0.87 for ANBI-ME; 0.83 for CV-ME) and between the methods (κ, 0.87 and 0.81 for each observer). ConclusionFor diagnosing colorectal lesions, an AA concentration of 4.5% in ANBI-ME was safe and effective. Its diagnostic performance was comparable to CV-ME, and future large-sample studies may confirm its potential as a reliable alternative endoscopic diagnostic method.

Exploring disease therapeutic strategies by modulating the TNF-/NF-KB signalling pathway

In recent years, the number of patients with rheumatic diseases such as ankylosing spondylitis has been increasing, with over 3 million young people suffering from the disease. One main causal factor for these diseases is tumour necrosis factor-a (TNF-a), a pro-inflammatory cytokine that triggers mutations and apoptosis of various cell types by activating multiple signalling pathways. NF-KB is an essential intracellular nuclear transcription factor that participates in the inflammatory and immune responses of the organism and regulates apoptosis and stress responses. Excessive activation of NF-KB has been linked to inflammatory changes in various human diseases, such as rheumatoid arthritis, cardiovascular diseases, etc. Therefore, inhibition of the NF-KB signalling pathway by drugs may become a therapeutic approach. Among multiple signalling pathways that induce inflammation, the TNF-/NF-KB signalling pathway is considered the most important. This review will discuss the relevant diseases for which modulation of the TNF-/NF-KB signalling pathway is the main therapeutic strategy. These strategies include targeted inhibition by blocking the TNF-a/NF-KB signalling pathway, modulation of inflammatory factor transmission at the nanoscale by ribonucleotides, and Oncolytic virus therapy. Some of these strategies are effective and have been applied to treat multiple diseases; others are still in the exploratory phase, but the potential of these exploratory therapies is enormous and promises to overcome the limitations of traditional therapeutic strategies.

The role of psychological safety in promoting software quality in agile teams

Psychological safety continues to pique the interest of scholars in a variety of disciplines of study. Recent research indicates that psychological safety fosters knowledge sharing and norm clarity and complements agile values. Although software quality remains a concern in the software industry, academics have yet to investigate whether and how psychologically safe teams provide superior results. In this study, we explore how psychological safety influences agile teams’ quality-related behaviors aimed at enhancing software quality. To widen the empirical coverage and evaluate the results, we chose a two-phase mixed-methods research design with an exploratory qualitative phase (20 interviews) followed by a quantitative phase (survey study, N = 423). Our findings show that, when psychological safety is established in agile software teams, it induces enablers of a social nature that advance the teams’ ability to pursue software quality. For example, admitting mistakes and taking initiatives equally help teams learn and invest their learning in their future decisions related to software quality. Past mistakes become points of reference for avoiding them in the future. Individuals become more willing to take initiatives aimed at enhancing quality practices and mitigating software quality issues. We contribute to our endeavor to understand the circumstances that promote software quality. Psychological safety requires organizations, their management, agile teams, and individuals to maintain and propagate safety principles. Our results also suggest that technological tools and procedures can be utilized alongside social strategies to promote software quality.

Re-Engineering Nubi: A Design-Driven Approach to Cultural Sustainability in Korean Fashion/Textile Practice

This study delves into the realm of traditional Korean fashion/textile practice, known as traditional nubi and Tongyeong nubi (t/T nubi), and re-engineers it from a contemporary fashion perspective within the framework of cultural sustainability. Drawing upon the systemic design approach, the project employed specific activities to trace, decode, and reconfigure t/T nubi following the phases of exploration, reframing, creation, and catalyzation, aiming for innovative design in the contemporary fashion space. The paper emphasizes a holistic and iterative process, based on learning the practice of t/T nubi from artisans, understanding and recontextualizing the future envisaged for t/T nubi, and delivering creative outcomes. Utilizing the interpretative model of cultural sustainability through craft, the study also analyzed the outcomes, identifying impacts such as rethinking craft culture, decoding craft roots, and influencing craft culture. Striking a balance between safeguarding the practice and fostering creativity was challenging; however, the approach serves as a valuable reference for designers and/or artisans of traditional craft practice, bridging their knowledge and design thinking process with the enduring sustainability of this heritage craft. It also demonstrates the potential for designers to leverage traditional craft practices to drive innovation in design, production, and business.

A modified Runge–Kutta optimization for optimal photovoltaic and battery storage allocation under uncertainty and load variation

The interest in incorporating environmentally friendly and renewable sources of energy, like photovoltaic (PV) technology, into electricity grids has grown significantly. These sources offer benefits, such as reduced power losses and improved voltage stability. To optimize these advantages, it is essential to determine optimal placement and management of these energy resources. This paper proposes an Improved RUNge–Kutta optimizer (IRUN) for allocating PV-based distributed generations (DGs) and Battery Energy Storage (BES) in distribution networks. IRUN utilizes three strategies to avoid local optima and enhance exploration and exploitation phases: a non-linear operator for smoother transitions, a Chaotic Local Search for thorough exploration, and diverse solution updates for refinement. The efficacy of IRUN is evaluated using 10 benchmark functions from the CEC’20 test suite, followed by statistical analysis. Next, IRUN is used to optimize the allocation of PVDG and BES to minimize energy losses in two standard IEEE distribution networks. The optimization problem is divided into two stages. In the first stage, the optimal size and the location of PV systems are calculated to meet peak load demand. In the second stage, considering time-varying load demand and intermittent PV generation, effective energy management of BES is employed. The effectiveness of IRUN is compared against the original RUN and other well-known optimization algorithms through simulation results. The comprehensive analysis demonstrates that IRUN outperforms the compared algorithms, making it a leading solution for optimizing PV distributed generation and BES allocation in distribution networks and the results show that the energy loss reduction reaches 63.54% and 68.19% when using PVand BES in IEEE 33-bus and IEEE 69 bus respectively.

Enhancing vehicle detection in intelligent transportation systems via autonomous UAV platform and YOLOv8 integration

This study highlights the evolving landscape of object detection methodologies, emphasizing the superiority of deep learning-based approaches over traditional methods. Particularly in intelligent transportation systems-related applications requiring robust image processing techniques, such as vehicle identification, localization, tracking, and counting within traffic scenarios, deep learning has gained substantial traction. The YOLO algorithm, in its various iterations, has emerged as a popular choice for such tasks, with YOLOv5 garnering significant attention. However, a more recent iteration, YOLOv8, was introduced in early 2023, ushering in a new phase of exploration and potential innovation in the field of object detection. Consequently, due to its recent emergence, the number of studies on YOLOv8 is extremely limited, and an application in the field of Intelligent Transportation Systems (ITS) has not yet found its place in the existing literature. In light of this gap, this study makes a noteworthy contribution by delving into vehicle detection using the YOLOv8 algorithm. Specifically, the focus is on targeting aerial images acquired through a modified autonomous UAV, representing a unique avenue for the application of this cutting-edge algorithm in a practical context. The dataset employed for training and testing the algorithm was curated from a diverse collection of traffic images captured during UAV missions. In a strategic effort to enhance the variability of vehicle images, the study systematically manipulated flight patterns, altitudes, orientations, and camera angles through a custom-designed and programmed drone. This deliberate approach aimed to bolster the algorithm's adaptability across a wide spectrum of scenarios, ultimately enhancing its generalization capabilities. To evaluate the performance of the algorithm, a comprehensive comparative analysis was conducted, focusing on the YOLOv8n and YOLOv8x submodels within the YOLOv8 series. These submodels were subjected to rigorous testing across diverse lighting and environmental conditions using the dataset. Through tests, it was observed that YOLOv8n achieved an average precision of 0.83 and a recall of 0.79, whereas YOLOv8x attained an average precision of 0.96 and a recall of 0.89. Furthermore, YOLOv8x also outperformed YOLOv8n in terms of F1 score and mAP, achieving values of 0.87 and 0.83 respectively, compared to YOLOv8n's 0.81 and 0.79. These outcomes of the evaluation illuminated the relative strengths and weaknesses of YOLOv8n and YOLOv8x, leading to the conclusion that YOLOv8n is well-suited for real-time ITS applications, while YOLOv8x exhibits superior detection capabilities.

Distributed random swap: An efficient algorithm for minimum sum-of-squares clustering

The clustering model known as Minimum Sum-of-Squares Clustering (MSSC) is widely used, with the popular k-means algorithm serving as its local minimizer. It is well-known that solutions of k-means can result in substantial deviations from the true global optimum of MSSC. While numerous heuristics and metaheuristics have been proposed to overcome this limitation, none have gained dominant acceptance in academic literature. This is likely related to challenges such as intricate implementations and a multitude of tunable parameters.In this paper, we dispute the belief that simplifying an algorithm for MSSC inherently means sacrificing quality. We present the Distributed Random Swap (DRS-means) algorithm, which is designed to enhance clustering performance for the MSSC problem. This algorithm can be interpreted as an iterative method that refines the solution generated by the k-means algorithm during each iteration. The enhancement is achieved by selecting points based on specific probability distributions. These distributions are carefully designed to improve and speed up the exploration phase. The proposed algorithm is straightforward to implement. DRS-means offers a user-friendly solution with state-of-the-art results, making it suitable for a wide range of research fields.

Modelling the nicotine pharmacokinetic profile for e-cigarettes using real time monitoring of consumers’ physiological measurements and mouth level exposure

Pharmacokinetic (PK) studies can provide essential information on abuse liability of nicotine and tobacco products but are intrusive and must be conducted in a clinical environment. The objective of the study was to explore whether changes in plasma nicotine levels following use of an e-cigarette can be predicted from real time monitoring of physiological parameters and mouth level exposure (MLE) to nicotine before, during, and after e-cigarette vaping, using wearable devices. Such an approach would allow an -effective pre-screening process, reducing the number of clinical studies, reducing the number of products to be tested and the number of blood draws required in a clinical PK study Establishing such a prediction model might facilitate the longitudinal collection of data on product use and nicotine expression among consumers using nicotine products in their normal environments, thereby reducing the need for intrusive clinical studies while generating PK data related to product use in the real world.An exploratory machine learning model was developed to predict changes in plasma nicotine levels following the use of an e-cigarette; from real time monitoring of physiological parameters and MLE to nicotine before, during, and after e-cigarette vaping. This preliminary study identified key parameters, such as heart rate (HR), heart rate variability (HRV), and physiological stress (PS) that may act as predictors for an individual’s plasma nicotine response (PK curve). Relative to baseline measurements (per participant), HR showed a significant increase for nicotine containing e-liquids and was consistent across sessions (intra-participant). Imputing missing values and training the model on all data resulted in 57% improvement from the original’learning’ data and achieved a median validation R2 of 0.70.The study is in its exploratory phase, with limitations including a small and non-diverse sample size and reliance on data from a single e-cigarette product. These findings necessitate further research for validation and to enhance the model's generalisability and applicability in real-world settings. This study serves as a foundational step towards developing non-intrusive PK models for nicotine product use.

Optimizing the Steering of Driverless Personal Mobility Pods with a Novel Differential Harris Hawks Optimization Algorithm (DHHO) and Encoder Modeling.

This paper aims to improve the steering performance of the Ackermann personal mobility scooter based on a new meta-heuristic optimization algorithm named Differential Harris Hawks Optimization (DHHO) and the modeling of the steering encoder. The steering response in the Ackermann mechanism is crucial for automated driving systems (ADS), especially in localization and path-planning phases. Various methods presented in the literature are used to control the steering, and meta-heuristic optimization algorithms have achieved prominent results. Harris Hawks optimization (HHO) algorithm is a recent algorithm that outperforms state-of-the-art algorithms in various optimization applications. However, it has yet to be applied to the steering control application. The research in this paper was conducted in three stages. First, practical experiments were performed on the steering encoder sensor that measures the steering angle of the Landlex mobility scooter, and supervised learning was applied to model the results obtained for the steering control. Second, the DHHO algorithm is proposed by introducing mutation between hawks in the exploration phase instead of the Hawks perch technique, improving population diversity and reducing premature convergence. The simulation results on CEC2021 benchmark functions showed that the DHHO algorithm outperforms the HHO, PSO, BAS, and CMAES algorithms. The mean error of the DHHO is improved with a confidence level of 99.8047% and 91.6016% in the 10-dimension and 20-dimension problems, respectively, compared with the original HHO. Third, DHHO is implemented for interactive real-time PID tuning to control the steering of the Ackermann scooter. The practical transient response results showed that the settling time is improved by 89.31% compared to the original response with no overshoot and steady-state error, proving the superior performance of the DHHO algorithm compared to the traditional control methods.

Experiences With Unionization Among General Surgery Resident Physicians, Faculty, and Staff

Labor unions are a mechanism for employee advocacy, but their role in surgery resident wellness is poorly characterized. To understand experiences with unionization among general surgery residents and residency program faculty and staff. This exploratory qualitative study included data from the Surgical Education Culture Optimization Through Targeted Interventions Based on National Comparative Data (SECOND) trial. In the exploratory phase of the SECOND trial (from March 6, 2019, to March 12, 2020), semistructured interviews about wellness were conducted with residents, faculty (attending physicians), and staff (program administrators) at 15 general surgery residency programs. Unionization was identified as an emergent theme in the interviews. Data analysis was performed from March 2019 to May 2023. The main outcome was resident and faculty experience with resident labor unions. In the qualitative analysis, lexical searches of interview transcripts identified content regarding resident labor unions. A codebook was developed inductively. Transcripts were coded by dyads, using a constant comparative approach, with differences reconciled by consensus. A total of 22 interview transcripts were identified with relevant content. Of these, 19 were individual interviews conducted with residents (n = 10), faculty (n = 4), administrative staff (n = 1), a program director (n = 1), a department chair (n = 1), and designated institutional officials (n = 2), and 3 were from resident focus groups. Residents from all postgraduate year levels, including professional development (ie, research) years, were represented. Interviewees discussed resident unions at 2 programs (1 recently unionized and 1 with a decades-long history). Interviewees described the lack of voice and the lack of agency as drivers of unionization ("Residents…are trying to take control of their well-being"). Increased salary stipends and/or housing stipends were the most concretely identified union benefits. Unanticipated consequences of unionization were described by both residents and faculty, including (1) irrelevance of union-negotiated benefits to surgical residents, (2) paradoxical losses of surgery department-provided benefits, and (3) framing of resident-faculty relationships as adversarial. Union executives were noted to be nonphysician administrators whose participation in discussions about clinical education progression may increase the time and effort to remediate a resident and/or reduce educators' will to meaningfully intervene. Active surgical resident participation within the union allows for an understanding of surgical trainees' unique needs and reduced conflict. In this qualitative study, unionization was a mechanism for resident voice and agency; the desire to unionize likely highlighted the lack of other such mechanisms in the training environment. However, these findings suggest that unionization may have had unintended consequences on benefits, flexibility, and teaching. Effective advocacy, whether within or outside the context of a union, was facilitated by participation from surgical residents. Future research should expand on this exploratory study by including a greater number of institutions and investigating the evolution of themes over time.

Determination of the Optimal Volume of 0.5% Ropivacaine in Single-Injection Retroclavicular Brachial Plexus Block for Arthroscopic Shoulder Surgery: A Phase I/II Trial.

A brachial plexus block plays an important role in providing perioperative analgesia for shoulder surgery; however, the inherent risk of phrenic nerve block and resulting hemidiaphragmatic paralysis may limit its use in patients with compromised pulmonary function. This study aimed to evaluate safety, efficacy, the maximum tolerated volume, and the optimal biological volume of 0.5% ropivacaine used in a single-injection retroclavicular brachial plexus block for arthroscopic shoulder surgery. In this seamless single-arm exploratory phase I/II trial, a novel Bayesian optimal interval design was used to guide volume escalation for determination of the maximum tolerated volume, followed by sequential volume expansion using Bayesian optimal phase 2 design to establish the optimal biological volume. Fifty-four patients who underwent arthroscopic shoulder surgery received a single-injection retroclavicular brachial plexus block with 0.5% ropivacaine ranging from 15 mL to 40 mL. The primary outcomes were complete or partial hemidiaphragmatic paralysis in phase I, measured using ultrasound 30 min after block completion, and the block success in phase II, defined as achieving a total sensorimotor score ≥12 points and the total sensory score ≥3, measured through manual sensorimotor testing. The maximum tolerated volume for the single-injection retroclavicular brachial plexus block was determined to be 35 mL of 0.5% ropivacaine, with a hemidiaphragmatic paralysis rate of 0.09 (95% credible interval, 0 to 0.29). The optimal biological volume was found to be 25 mL, with a block success rate of 1.0 (95% credible interval, 0.95 to 1.0) and a negligible hemidiaphragmatic paralysis rate of 0.01 (95% credible interval, 0 to 0.06). A single-injection retroclavicular brachial plexus block using 25 mL of 0.5% ropivacaine produced consistent block success with a minimal HDP rate, suggesting the need for further studies to confirm this result in arthroscopic shoulder surgery.

A novel memory-based artificial gorilla troops optimizer for installing biomass distributed generators in unbalanced radial networks

Optimizing unbalanced distribution networks through the strategic integration of distributed generators (DGs) has long been recognized as a significant challenge. Selecting the optimal sizes and locations for these generators is crucial for minimizing network power loss and enhancing voltage profiles. The previously published methods have been plagued by issues such as slow convergence rates, entrapment in local optima, complexity, and extensive computational requirements. Addressing these limitations, this paper introduces an efficient methodology: the Memory-based Artificial Gorilla Troops Optimizer (MGTO). This approach leverages memory-based mechanisms to enhance exploration and decision-making, facilitating the seamless integration of various biomass DGs (BDGs) into unbalanced IEEE 37-bus radial networks. The immigration of gorillas during the exploration phase is enriched through the utilization of stored memories of candidate trajectories within the search space, enabling the silverback to make informed decisions. Furthermore, a multi-objective variant of MGTO is developed in collaboration with Fuzzy Decision-Making (FDM), allowing for the simultaneous optimization of multiple targets. To demonstrate the MGTO effectiveness, it is rigorously compared against a comprehensive set of established optimization algorithms, including the Honey Badger Algorithm (HBA), Runge Kutta Optimizer (RUN), and others. The results proved the dominance of the proposed MGTO by getting minimum power loss and voltage fluctuation of 0.364 % and 15.4 %, respectively, while in the multi-objective problem, the best results are 0.513 % loss and 17.9% voltage fluctuation. The results proved the consistency of the proposed MGTO in installing different BDGs into an unbalanced distribution network.