Initial Objective Research Articles

Spatial Patterns of Solar Photovoltaic System Diffusion Kisumu County, Kenya

Purpose: This research aligns with Sustainable Development Goal 7, contributing to the progress outlined in the 2030 Agenda for Sustainable Development and the commitments of the Paris Climate Agreement. Specifically, this study focuses on the spatial analysis of solar photovoltaic (PV) systems, offering valuable insights for academic exploration and informing public policy decisions related to the widespread adoption of this increasingly vital renewable energy technology. The outcomes of this project transcend academic significance, extending to practical applications for energy practitioners, policymakers, academics, and future researchers. The meticulous tracking of solar PV system spatial patterns in Kisumu County yields data that not only benefits its residents but also serves as a valuable resource for the entire nation. This information will be instrumental for current energy practitioners, policymakers, academicians, and prospective researchers seeking to advance the collective knowledge in this field.
 Methodology: The study adopted a Quasi-Experimental research design to explore various social phenomena, aiming to identify key facts. Utilizing statistical evidence, we conducted numerical comparisons and statistical inferences to validate or refute the research questions. Locational information on households utilizing small home systems was extracted from a secondary Solar Database. This data underwent georeferencing, enhancing our comprehension of the actual geographical distribution of households and facilitating the achievement of our research objectives. In the process of data analysis, we employed inferential statistics, specifically regression analysis, conducted using ArcGIS PRO powered by ESRI. The utilization of ArcGIS Pro extended to the creation of an empirical model. This model was designed to probe into the factors influencing the observed spatial diffusion patterns, providing a robust analytical framework for our investigation.
 Findings: In the initial objective, cluster and outlier analysis unveiled a distinct low-high cluster pattern for solar home systems (SHS). The optimized hotspot analysis consistently identified SHS hotspots and cold spots within the region, particularly aligning with urban areas, notably Kisumu. The second objective exposed factors influencing diffusion, revealing negative correlations with population density, household density, and poverty rate, indicating diminished adoption in densely populated and impoverished areas. Conversely, positive correlations with income, education, and electrification rates signaled heightened adoption in wealthier, educated communities. Despite consistent diffusion trends, an empirical model underscored the substantial impact of income and electricity on SHS diffusion. The third objective disclosed that between 2016-2021, SHS diffusion contributed to the mitigation of 268,581.6 metric tons of carbon emissions.
 Unique Contribution to Theory, Practice and Policy: This research makes a distinctive contribution to theory by delving into the impact of solar home systems (SHS) in Kenya, particularly within the context of the country's commitment to reduce greenhouse gas (GHG) emissions. The theoretical foundation lies in addressing the existing gap in understanding the spatial distribution and diffusion patterns of SHS and their role in GHG reduction, aligning with Kenya's focus on renewable energy adoption.

A Novel Fully Automatic Concept to Produce First Subset of Bowden Cables, Improving Productivity, Flexibility, and Safety

With a view to maintaining the competitiveness required by the market, the automotive industry strongly encourages its suppliers to develop new production methods and technologies capable of reducing the costs of produced products, ensuring the necessary quality, and increasing flexibility, with a view to responding more easily to the customization of the products that the market increasingly demands. The main goal of this work was to increase the flexibility and productivity of equipment capable of producing the first subset that constitutes the product commonly known as the Bowden cable. To this end, the design science research methodology was used, which was understood as the most effective in describing scientific work related to the improvement of existing systems. Bowden cables are cables that activate various devices in the car, such as opening doors, moving window glasses, and adjusting some car seats, among others. The work consisted of integrating several operations usually carried out for the manufacture of the referred subset, reducing logistics operations and manual work, increasing operator safety, and increasing the production rate and flexibility of the equipment, by reducing the setup time. For this purpose, new mechanical concepts were developed, and automation was applied, which resulted in a completely new concept, able to fulfill all the objectives initially set. It should be noted here that the new equipment allowed a production rate of 1140 p/h, when the initial objective was 1100 p/h; it requires an investment of only around EUR 55,000 (easy return on investment), occupies only 11.6 m2, and has reinforced safety systems to avoid workers’ injuries, an aspect that is very important in this type of equipment, where operators deal with cutting systems and high temperatures. The dissemination of this concept could help other researchers to easily find solutions to certain problems that they face in the development of modern equipment. The main contributions of this paper are the novel concepts created to overcome some process difficulties, which can be used for a wide range of other processing situations with similar difficulties. The solutions proposed allow a decrease in the cycle time, present high flexibility, save workshop space, and are affordable in terms of global cost.

A DeturNet-Based Method for Recovering Images Degraded by Atmospheric Turbulence

Atmospheric turbulence is one of the main issues causing image blurring, dithering, and other degradation problems when detecting targets over long distances. Due to the randomness of turbulence, degraded images are hard to restore directly using traditional methods. With the rapid development of deep learning, blurred images can be restored correctly and directly by establishing a nonlinear mapping relationship between the degraded and initial objects based on neural networks. These data-driven end-to-end neural networks offer advantages in turbulence image reconstruction due to their real-time properties and simplified optical systems. In this paper, inspired by the connection between the turbulence phase diagram characteristics and the attentional mechanisms for neural networks, we propose a new deep neural network called DeturNet to enhance the network’s performance and improve the quality of image reconstruction results. DeturNet employs global information aggregation operations and amplifies notable cross-dimensional reception regions, thereby contributing to the recovery of turbulence-degraded images.

Development of the Extracorporeal Life Support Organization International Adult Extracorporeal Membrane Oxygenation Curriculum: Creation of Initial Learning Objectives via a Modified Delphi Process

Development of the Extracorporeal Life Support Organization International Adult Extracorporeal Membrane Oxygenation Curriculum: Creation of Initial Learning Objectives via a Modified Delphi Process

A systematic tomography framework for thickness mapping of pipes using helical guided waves

Pipe wall loss caused by corrosion is of growing interest in the petrochemical industry. A systematic tomography framework using helical guided waves is developed in this paper to conduct a thickness mapping. In this work, the thickness under investigation is reconstructed using an objective function derived from the acoustic Helmholtz equation. The main approach consists of two parts. Firstly, the parametric dictionary is designed to separate the overlapped guided waves travelling in helical paths. After that, the scattering field can be extracted as the input of the distorted born iteration method. The imaging result is exemplified numerically and experimentally, with the strengths and drawbacks explained thoroughly. Remarkably, the thickness error of the simple defect is still within 0.5 mm when the input data is poor. A clear qualitative description of complex defects can be achieved through iterations even in the absence of an initial objective function. The framework established in this paper contributes a comprehensive imaging algorithm and the corresponding signal processing approach, all of which are conducive to providing some reference for engineering applications in nondestructive testing and structural health monitoring.

A Review of Evaluative Measures of Carbon-Neutral Buildings: The Bibliometric and Science Mapping Analysis towards Sustainability

This study aims to comprehensively depict a thematic evaluation within the context of carbon-neutral buildings over this century at variable time phases (2000–2008, 2009–2016, and 2017–2023). The overarching objectives of this study are delineated into three (3) contexts. Firstly, a bibliometric network encompassing influential research documents, authors, prominent journals, organisations, and countries is erected in pertinent fields. Secondly, significant terms are extracted from the scientific literature to exhibit co-occurrence patterns. Finally, an analysis of the evaluative clusters across variable phases was conducted to ascertain their intricate interrelations. The software tool VOSviewer Version 1.6.19 successfully achieves the initial objectives by visualising networks based on co-authorship, citations, co-citations, and bibliographic coupling. The ultimate goal of this research is fully realised through the application of the Science Mapping Analysis Tool (SciMAT), Version 3, which facilitated the evaluation of diverse clusters, phases, and thematic domains. The findings from the initial stages of research conducted on carbon-neutral buildings primarily revolve around energy-savings measures, environmental impacts, and the pursuit of energy-efficient design. As the research progressed into subsequent phases, the scope of inquiry broadened into specific themes, such as (1) optimisation, (2) retrofitting, (3) transitioning, and exploring (4) phase change materials (PCMs). Moreover, the areas of study continued to expand by developing diverse scenarios, algorithms, and digital twin technologies. The graphical representations of the strategic diagrams, evaluation areas, and cluster networks are a valuable resource for practitioners and policymakers, offering valuable insight and understanding of the multifaceted landscape of thematic evaluation in carbon-neutral buildings, thus facilitating further investigations and informed decision making.

Third Argentine Consensus statement on management Bipolar Disorders. Section3: Bipolar Disorder in the context of special situations

This document constitutes the third and last part of the Third Argentine Consensus on the Management of Bipolar Disorders carried out by the Argentine Association of Biological Psychiatry (AAPB). Continuing with the initial objective, this section of the Consensus on the Management of Bipolar Disorders is focused on the management of bipolar disorders in special populations. This section constitutes a comprehensive review and expert consideration of the scientific evidence on: a) the management of bipolar disorders in treatment-resistant patients; b) the management of bipolar disorder in childhood and adolescence; c) the management of bipolar disorders in women during their perinatal period and, d) the management of bipolar disorders in older adults.

Robot Cooking—Transferring Observations into a Planning Language: An Automated Approach in the Field of Cooking

The recognition of human activities from video sequences and their transformation into a machine-readable form is a challenging task, which is the subject of many studies. The goal of this project is to develop an automated method for analyzing, identifying and processing motion capture data into a planning language. This is performed in a cooking scenario by recording the pose of the acting hand. First, predefined side actions are detected in the dataset using classification. The remaining frames are then clustered into main actions. Using this information, the known initial positions and virtual object tracking, a machine-readable planning domain definition language (PDDL) is generated.

Universal constructions for Poisson algebras. Applications

We introduce the universal algebra of two Poisson algebras P and Q as a commutative algebra A:=P(P,Q) satisfying a certain universal property. The universal algebra is shown to exist for any finite dimensional Poisson algebra P and several of its applications are highlighted. For any Poisson P-module U, we construct a functor U⊗−:AM→QPM from the category of A-modules to the category of Poisson Q-modules which has a left adjoint whenever U is finite dimensional. Similarly, if V is an A-module, then there exists another functor −⊗V:PPM→QPM connecting the categories of Poisson representations of P and Q and the latter functor also admits a left adjoint if V is finite dimensional. If P is n-dimensional, then P(P):=P(P,P) is the initial object in the category of all commutative bialgebras coacting on P. As an algebra, P(P) can be described as the quotient of the polynomial algebra k[Xij|i,j=1,⋯,n] through an ideal generated by 2n3 non-homogeneous polynomials of degree ≤2. Two applications are provided. The first one describes the automorphisms group AutPoiss(P) as the group of all invertible group-like elements of the finite dual P(P)o. Secondly, we show that for an abelian group G, all G-gradings on P can be explicitly described and classified in terms of the universal coacting bialgebra P(P).

Developing a Global Library of Underwater Biological Sounds and a World Oceans passive acoustic monitoring day

The volumes of “Ocean Sound” recordings now being collected in aquatic systems around the world have reached a level that has necessitated a new wave of data sharing, processing, and analysis techniques to help detect, identify, and assess an increasing number of known and unknown sound sources and sound types. These new tools are matched with a need for reference material to assist the rapidly growing bioacoustics research community, and a process to standardize how they are characterized and reported. A working group of the International Quiet Ocean Experiment proposed the development of a Global Library of Underwater Biological Sound (GLUBS) to integrate new and existing applications to help address these needs. Two initial objectives of GLUBS have been to increase awareness of unknown sounds and to develop a community-wide project that can broaden our understanding of aquatic soundscapes around the world. To that end, we discuss recent developments in designing GLUBS, incorporating underwater sonifery as a searchable trait on the World Register of Marine Species, initiating a recent global community effort to simultaneously record aquatic soundscapes on World Oceans Day, and a drive to agree on a standard process for characterizing newly described sounds.

Mobile Tourism Recommender System for Users to Get a Better Choice of Tour

The system might include a turn-by-turn route highlight to prevent fake preferences that check if the user has taken the course. A larger customer overview with more participants is required to acquire more insightful client feedback. Our ex-amination was designed as a lab experiment to gather initial data straight absent. While making fun of other clients and their system comments, we looked at a few initial objective mixtures. Doing field research with actual clients using our suggested model in real-world situations (such as when looking for a course online to work from home) is crucial. This will help us better understand how effective our approach is. In this article, we developed a creative method for recommending multimodal travel routes. In a client survey with 20 participants, we evaluated the applicability of our cross-breed computation and its usability. The results show that CF, in-formation-based, and well-liked course concepts complement one more successfully than cutting-edge course organizer advances. Thanks to the Google Guides Programming interface, our application can give seven different elective trip options.

Application of the Integrated Deep Reinforcement Learning Solution in Automatic IMRT Planning for Gastric Cancer

The intensity-modulated radiation therapy (IMRT) treatment planning optimization process is usually a manual, tedious and time-consuming task. An unsupervised integrated automatic IMRT planning solution was discussed and implemented to simulate the manual operation during the whole planning process. Based on knowledge-based planning (KBP) and deep reinforcement learning (DRL) schemes, a novel integrated solution combining the multi-objectives optimization policy network (MOPN) and three-dimensional dose prediction module (3D-DPM) was proposed. The MOPN was trained to learn how to adjust multiple optimization objectives in commercial Eclipse treatment planning system (TPS). The 3D-DPM was developed to generate the patient-specific initial optimization objectives to reduce the overall exploration space during MOPN training. The Eclipse 15.6 TPS Scripting Application Programming Interface (ESAPI) was used to realize the automatic interaction between models and TPS. In this study, 100 previously treated gastric cancer cases were selected from the clinical database, 70 cases were used for the training of dose prediction model and MOPN, the remaining 30 cases for evaluating the feasibility and effectiveness of automatic treatment planning solution. For all tested cases, the complete automatic treatment plan for a new case was generated based on the integrated solution, with about 6 min. Compared with the MOPN initial plans, the actual dose of spinal cord, liver, kidney-left, kidney-right, and intestine in the MOPN final plans reduced 42.4%, 25.2%, 52.4%, 37.8%, 11.8% respectively. The dose result of OARs in the MOPN final plans was similar to those in the clinical plans. In addition, a new case was able to complete optimization with about 15 adjustment steps by the trained MOPN. We successfully developed an integrated solution for automatic treatment planning. It first includes the dose prediction module for obtaining patient-specific initial optimization objectives. We demonstrated that the trained MOPN can mimic the operations of the physicians to adjust multiple objectives and obtain a high-quality plan in a shorter time. This integrated solution contributes to improving the efficiency of the overall planning workflow and reducing the variation of plan quality in clinical practice. Although improvement is warranted, the proposed integrated solution is a promising practical and effective approach for automatic planning in commercial TPS.

Analysis of Quality and Quantity of Complementary Feeding and Nutrition among Children of 6 to 36 Months in Maiduguri Metropolitan Council, Borno State, Nigeria

This paper provides a cross sectional analysis of the quality and quantity of complimentary feeding and nutrition among children of 6-36 months in Maiduguri Metropolitan Council. The role of health workers and parents in contextualities in driving, constraining or otherwise influencing complimentary feeding practices is explored through a review of the essential literature. Though this literature is found to have considerably expanded the scope of understanding around health and nutrition, research in the area is found to be lacking in methodological coherence and theoretical substance. Future efforts are needed to systematically bring together the array of insights, methodological approaches, and recommendations in this literature, as well as better bound, differentiate and systemize health and nutrition research in the area going forward. Two initial objectives are advanced through this paper in relation to this dual research imperative. They employed the survey research method. It revealed that knowledge of complementary feeding amongst women in emergencies are poor and government need to scale up support through local enlightenment programs that will boost good health and nutritional practices. Keywords: Complementary Feeding, Nutrition, Quality of complimentary feeding, Maiduguri Metropolitan Council, Nigeria.

Optimization of an Inductive Displacement Transducer.

This paper presents the optimization of an inductive displacement transducer or linear variable differential transformer (LVDT). The method integrates design software (SolidWorks 2023), simulation tools (COMSOL Multiphysics), and MATLAB. The optimization phase utilizes the non-dominated sorting genetic algorithm (NSGA)-II and -III to fine-tune the geometry configuration by adjusting six inner parameters corresponding to the dimension of the interior components of the LVDT, thus aiming to improve the overall performance of the device. The outcomes of this study reveal a significant achievement in LVDT enhancement. By employing the proposed methodology, the operational range of the LVDT was effectively doubled, extending it from its initial 8 (mm) to 16 (mm). This expansion in the operational range was achieved without compromising measurement accuracy, as all error values for the working range of 0-16 (mm) (NSGA-II with a maximum final relative error of 2.22% and NSGA-III with 2.44%) remained below the imposed 3% limit. This research introduces a new concept in LVDT optimization, capitalizing on the combined power of NSGA-II and NSGA-III algorithms. The integration of these advanced algorithms, along with the interconnection between design, simulation, and programming tools, distinguishes this work from conventional approaches. This study fulfilled its initial objectives and generated quantifiable results. It introduced novel internal configurations that substantially improved the LVDT's performance. These achievements underscore the validity and potential of the proposed methodology in advancing LVDT technology, with promising implications for a wide range of engineering applications.

Quantum Chemical Investigation of the Interaction of Thalidomide Monomeric, Dimeric, Trimeric, and Tetrameric Forms with Guanine DNA Nucleotide Basis in DMSO and Water Solution: A Thermodynamic and NMR Spectroscopy Analysis.

Thalidomide (TLD) was used worldwide as a sedative, but it was revealed to cause teratogenicity when taken during early pregnancy. It has been stated that the (R) enantiomer of TLD has therapeutic effects, while the (S) form is teratogenic. Clinical studies, however, demonstrated the therapeutic efficacy of thalidomide in several intractable diseases, so TLD and its derivatives have played an important role in the development and therapy of anticancer drugs. Therefore, it is important to know the molecular mechanism of action of the TLD, although this is still not clear. In what molecular interactions are concerned, it is known that drug molecules can interact with DNA in different ways, for example, by intercalation between base pairs. Furthermore, the ability of the TLD to interact with DNA has been confirmed experimentally. In this work, we report a theoretical investigation of the interaction of the R and S enantiomers of TLD, in its monomeric, dimeric, trimeric, and tetrameric forms, with guanine (GUA) DNA nucleotide basis in solution using density functional theory (DFT). Our initial objective was to evaluate the interaction of TLD-R/S with GUA through thermodynamic and spectroscopic study in dimethyl sulfoxide (DMSO) solvent and an aqueous solution. Comparison of the experimental 1H nuclear magnetic resonance (NMR) spectrum in DMSO-d6 solution with calculated DFT-PCM-DMSO chemical shifts revealed that TLD can undergo molecular association in solution, and interaction of its dimeric form with a DNA base ((TLD)2-GUA and (TLD)2-2GUA, for example) through H-bond formation is likely to take place. Our results strongly indicated that we must consider the plausibility of the existence of TLD associations in solution when modeling the complexation of the TLD with biological targets. This is new information that may provide further insight into our understanding of drug binding to biological targets at the molecular level.

Super-resolution DOA estimation methods based on element multiplication and summation

In this article, we propose two direction-of-arrival (DOA) estimation methods to further improve the resolution and performance of subspace-based algorithms. While most subspace-based algorithms employ either noise or signal subspaces, we utilize the signal and noise subspaces jointly. First, we project the steering vectors to the estimated signal and noise subspaces respectively to obtain the projection vectors, then, by investigating the numerical characteristics behind the inner products between the steering vectors and the estimated eigenvectors of the sample covariance matrix, an initial estimator is proposed based on the multiplication of elements of the projection vectors instead of the summation. Though the initial estimator achieves higher resolution than most existing subspace-based methods, the appearance of pseudo peaks, especially in high SNR, may affect the estimation accuracy. By analyzing the main elements in the initial objective function that cause pseudo peaks, two improved algorithms incorporating element summation are proposed to balance the resolution capacity and estimation accuracy, which are especially suitable for small sample size, low SNR and closely spaced sources. The numerical simulations verify the effectiveness and superiority of the proposed algorithms.

Preserving cognitive function in patients with Alzheimer's disease: The Alzheimer's disease neuroprotection research initiative (ADNRI).

The global trend toward aging populations has resulted in an increase in the occurrence of Alzheimer's disease (AD) and associated socioeconomic burdens. Abnormal metabolism of amyloid-β (Aβ) has been proposed as a significant pathomechanism in AD, supported by results of recent clinical trials using anti-Aβ antibodies. Nonetheless, the cognitive benefits of the current treatments are limited. The etiology of AD is multifactorial, encompassing Aβ and tau accumulation, neuroinflammation, demyelination, vascular dysfunction, and comorbidities, which collectively lead to widespread neurodegeneration in the brain and cognitive impairment. Hence, solely removing Aβ from the brain may be insufficient to combat neurodegeneration and preserve cognition. To attain effective treatment for AD, it is necessary to (1) conduct extensive research on various mechanisms that cause neurodegeneration, including advances in neuroimaging techniques for earlier detection and a more precise characterization of molecular events at scales ranging from cellular to the full system level; (2) identify neuroprotective intervention targets against different neurodegeneration mechanisms; and (3) discover novel and optimal combinations of neuroprotective intervention strategies to maintain cognitive function in AD patients. The Alzheimer's Disease Neuroprotection Research Initiative's objective is to facilitate coordinated, multidisciplinary efforts to develop systemic neuroprotective strategies to combat AD. The aim is to achieve mitigation of the full spectrum of pathological processes underlying AD, with the goal of halting or even reversing cognitive decline.

De l’évolution des représentations du handicap à l’évolution des modèles de conception des appareillages

The device occupies an important place in the therapeutic arsenal used in cerebral palsy. The molded seat (or seat corset), the pelvic-pedal splints for verticalization, the night splints represent the daily life of a large number of cerebral palsy subjects. Unfortunately, it is a source of pain or discomfort, which limits its use when the initial objectives seemed relevant. The tools allowing the development of the specifications of these devices have evolved in connection with the representation that we have of disability and of the person with a disability. This evolution opens up new perspectives in the representation and design of the device: This would no longer be only a tool for compensating for a deficiency but that of an adaptive and enabling tool.

Gendered Socio-Economic Integration: Lessons From the Syria Response

Despite increasing awareness, there are gaps in implementers' policy initiatives and ability to effectively deploy programs that incorporate gender mainstreaming in refugee contexts. Challenges impacting the design and implementation of gender sensitive livelihoods programming are identified based on forty-eight interviews with UN and (I)NGO staff in Jordan, Lebanon, Turkey, and the Kurdistan Region of Iraq (KRI). Problems include a lack of gender expertise, internalized gender stereotypes, lack of donor commitment to gender goals, not being able to measure outcomes, and/or time constraints. We highlight best practices that make programs that help women refugees obtain work more successful. At the same time, interviewees raised important questions concerning the appropriate metrics for measuring the success of ‘livelihoods' initiatives, arguing that social integration, increasing women’s soft skills and self-confidence were just as or more important than economic objectives of many initiatives being rolled out.

Impacts of environmental decontamination on the rebuilding of returnees' lives after the Fukushima accident.

Environmental decontamination after a nuclear disaster not only contributes to reducing the public's exposure to radiation, it also introduces waste disposal issues arising from the decontamination process. In addition to that issue, the optimisation of decontamination efforts necessitates the consideration of various environmental, economic, and societal factors. Stakeholders' perspectives are important for identifying the multifaceted aspects to be considered. We conducted a semi-structured interview survey in 2019 with ten residents in a rural community in Fukushima, Japan, which experienced a six-year-long evacuation due to the 2011 Fukushima Daiichi Nuclear Power Plant accident. The main survey questions addressed returnees' perceptions of the decontamination of farmlands and forests. The 'Steps for Coding and Theorization' procedure was used for qualitative analysis. The analysis illuminated the positive and negative impacts of the decontamination process on the rebuilding of the returnees' rural lives from various perspectives and identified elements to be considered for the optimisation of future remediation efforts. The removal of radioactive materials had a positive psychological impact on the returnees, fostering a sense of security that their crops were safe and instilling confidence that the high-quality environment of the region had been restored. These aspects were not included among the initial governmental objectives for decontamination, which were aimed solely at reducing radiation exposure. By contrast, the removal of fertile topsoil from farmland had a negative impact on the residents, making them hesitant to resume farming. Our findings suggest that emphasising procedural fairness in decision-making of decontamination options such as reflection of stakeholders' opinions led to residents perceiving their post-decontamination situation more positively. Our results provide valuable insights for optimising remediation strategies for the recovery process following a significant nuclear accident.