The supplementary material, part of the online version, is available via the link 101007/s12310-023-09589-8.
At 101007/s12310-023-09589-8, the online version provides supplementary material.

Software-centric organizations establish loosely coupled organizational structures, meticulously replicating this structure across business processes and information systems, guided by strategic aims. Crafting business strategies in a model-driven development context is complex because key aspects such as organizational structure and strategic ends and means are usually handled within the enterprise architecture framework for achieving organizational alignment, without being integrated as requirements into MDD methods. In order to resolve this obstacle, researchers have formulated LiteStrat, a business strategy modeling technique compliant with MDD for the design of information systems. The empirical comparison of LiteStrat with i*, a commonly used strategic alignment model in the context of MDD, is the subject of this article. Through a literature review on the experimental comparison of modeling languages, this article also proposes a study to assess and compare the semantic quality of modeling languages, backed by empirical data analyzing the differences between LiteStrat and i*. 28 undergraduate subjects participate in the evaluation process, which utilizes a 22 factorial experiment. Models using LiteStrat displayed a noteworthy increase in accuracy and comprehensiveness, with no differences found in modeller efficiency and satisfaction metrics. These results support the use of LiteStrat for modeling business strategies within a model-driven framework.

To obtain tissue samples from subepithelial lesions, mucosal incision-assisted biopsy (MIAB) has been proposed as a replacement for endoscopic ultrasound-guided fine-needle aspiration. Despite this, minimal documentation exists regarding MIAB, and the available evidence is notably weak, particularly in the context of small-sized lesions. Our case series assessed the technical efficacy and the post-procedure consequences of MIAB for gastric subepithelial lesions, with a minimum size of 10 mm.
In a retrospective review at a single institution, cases of gastrointestinal stromal tumors, possibly exhibiting intraluminal growth, that underwent minimally invasive ablation (MIAB) between October 2020 and August 2022, were examined. The procedure's technical success, associated adverse events, and subsequent clinical outcomes were examined.
In a cohort of 48 cases of minimally invasive abdominal biopsy (MIAB), featuring a median tumor diameter of 16 millimeters, tissue sampling achieved a success rate of 96%, while the diagnostic accuracy reached 92%. Two biopsies were deemed adequate for a conclusive diagnosis. Of the cases observed, 2% (one case) showed postoperative bleeding. bloodstream infection Following miscarriages, a median of two months elapsed before 24 surgeries were performed, with no unfavorable findings observed intraoperatively due to the miscarriages. Finally, 23 cases were diagnosed with gastrointestinal stromal tumors via histological examination, and no patient who had MIAB showed signs of recurrence or metastasis during a median observation period of 13 months.
Gastric intraluminal growth types, potentially including small gastrointestinal stromal tumors, were successfully diagnosed using MIAB, which proved to be a feasible, safe, and useful approach. There were practically no observable clinical effects following the procedure.
Analysis of the data indicates that MIAB presents a feasible, safe, and beneficial strategy for histological assessment of intraluminal gastric growths, potentially gastrointestinal stromal tumors, even those of small size. Clinically, the effects of the procedure were considered to be negligible.

Artificial intelligence (AI) holds potential as a practical tool for the image classification of small bowel capsule endoscopy (CE). In spite of that, the development of a functional AI model proves to be a formidable obstacle. To better understand the complexities in modeling small bowel contrast-enhanced imaging, we developed an object detection computer vision model along with the necessary dataset.
A total of 18,481 images were obtained from 523 small bowel contrast-enhanced procedures performed at Kyushu University Hospital between September 2014 and June 2021. We compiled a dataset by annotating 12,320 images containing 23,033 disease lesions, and uniting them with 6,161 normal images, to examine the resulting dataset's characteristics. The dataset served as the basis for creating an object detection AI model using YOLO v5; subsequently, validation procedures were performed on this model.
The dataset's annotations comprised twelve types, and overlapping annotation types were evident in numerous images. 1396 images were used to validate our AI model, revealing a sensitivity of 91% for all 12 annotation types. A performance analysis recorded 1375 accurate identifications, 659 incorrect identifications, and 120 missed identifications. Individual annotations demonstrated a remarkable 97% sensitivity, coupled with an impressive area under the receiver operating characteristic curve of 0.98. However, detection quality fluctuated according to the nuances of each annotation.
AI-driven object detection employing YOLO v5 in small bowel contrast-enhanced imaging (CE) may facilitate effective and easily understood interpretations of the images. The SEE-AI project features a publicly accessible dataset, the AI model's weights, and a demonstration that illustrates our AI's functioning. We are eager to refine the AI model further in the future.
Employing YOLO v5 object detection algorithms in small bowel CE studies promises improved ease and clarity in the interpretation of radiological findings. The SEE-AI project provides access to our dataset, AI model weights, and a sample demonstration of our AI. In the future, we aim to further enhance the AI model's capabilities.

We explore the efficient hardware implementation of feedforward artificial neural networks (ANNs) within this paper, utilizing approximate adders and multipliers. Parallel architectures with large area requirements necessitate the employment of a time-multiplexed ANN implementation, thereby reusing computing resources in multiply-accumulate (MAC) units. The efficient hardware implementation of ANNs results from the replacement of precise adders and multipliers in MAC units with approximate versions, taking into account hardware precision requirements. Furthermore, a method for estimating the approximate count of multipliers and adders is presented, contingent upon the anticipated precision. For illustrative purposes within this application, the MNIST and SVHN databases are examined. To quantify the merit of the suggested method, several artificial neural network forms and setups were built and compared. click here Experimental outcomes indicate a smaller area and reduced energy consumption for ANNs created using the proposed approximate multiplier when contrasted with networks designed using previously prominent approximate multipliers. Observations indicate that utilizing approximate adders and multipliers concurrently yields, respectively, a potential energy reduction of up to 50% and an area reduction of up to 10% in the ANN design, alongside a slight deviation or improved hardware accuracy compared to the use of exact adders and multipliers.

A multitude of forms of loneliness are encountered by those in the health care profession (HCPs). They must be empowered with the courage, expertise, and instruments to address loneliness, particularly the existential kind (EL), which delves into the meaning of existence and the fundamental nature of living and dying.
Our research objective was to examine healthcare professionals' opinions about loneliness in the elderly, focusing on their understanding, perception, and professional experiences with emotional loneliness in the older population.
Five European countries' healthcare providers, a total of 139, participated in audio-recorded focus groups and individual interviews. Average bioequivalence Local analysis of the transcribed materials adhered to a pre-defined template. The results of participating nations were subsequently translated, combined, and inductively analyzed via standard content analysis techniques.
The participants described loneliness in multiple forms; a negative, unwanted type characterized by suffering, and a positive, desired form that involves a preference for solitude. Results showed a variation in the level of knowledge and comprehension of EL held by healthcare providers. EL was primarily connected by HCPs to various types of loss, including loss of autonomy, independence, hope, and faith, as well as feelings of alienation, guilt, regret, remorse, and concerns about the future.
HCPs voiced a desire to cultivate greater sensitivity and self-assuredness to effectively participate in existential conversations. They underscored the imperative to broaden their knowledge and comprehension of the topics of aging, death, and dying. Following the findings, a training program was designed to enhance knowledge and comprehension of the circumstances affecting older individuals. Within the program, practical conversational skills are cultivated, addressing emotional and existential aspects, consistently examining the introduced themes. Users can obtain the program from the designated website, www.aloneproject.eu.
The health care providers expressed a necessity for developing heightened sensitivity and self-assuredness to facilitate substantial existential conversations. They voiced the requirement to extend their comprehension of the process of aging, the inevitability of death, and the subject of dying. These data points have facilitated the design of a training program meant to deepen comprehension and knowledge of the circumstances affecting older people. Based on recurrent reflections on the presented subjects, the program features practical training in discussions concerning emotional and existential themes.