Applications in Resection Margins Evaluation in Oncological Laryngeal Surgery Through Narrow Band Imaging

DergiPark: 379008tmsjAims: The objective of the study is to demonstrate the utilization of refined optic technology such as narrow band imaging in the intraoperative visualization and the circumference of the lesion. The resection/ablation line of the tumor is established according to these two parameters. The operative strategy is based on the premise that a better imaging of the tumor will allow more accurate resection margins to be obtained from an oncological point of view. Methods: This is a prospective study that lasted for 15 months and includes 50 patients with stage T1-T3 laryngeal cancer who, according to the European Laryngology Society criteria, were eligible for endoscopic endolaryngeal resection. The suspended microlaryngoscopy was used under general anaesthesia and the CO2-LASER for the resection of the cancerous lesion. The resection margins were defined through illumination of the surgical field with the endoscopic narrow band imaging polarized light. Tissue fragments prelevations were obtained from the resection margins defined by the narrow band imaging and extemporaneous control examinations were made.Results: Narrow band imaging technique has led to more accurate and secure resection margins from an oncological point of view after laryngeal endoscopic resection with CO2-LASER or with cold instruments, therefore lowering the risk of tumor remaining and relapse.Additionally, this technique avoids the necessity of a second surgical intervention thus decreasing the patient’s risks and hospitalization costs. Furthermore, a significant benefit is the limitation of the healthy peritumoral tissue excision hence a superior functional prognostic. Conclusion: Narrow band imaging associated with laryngeal endoscopic surgery plays an essential role in precancerous and cancerous laryngeal pathology helping the surgeon accomplish both goals - curative surgery and functional result

Generative Benchmark Creation for Table Union Search

Data management has traditionally relied on synthetic data generators to generate structured benchmarks, like the TPC suite, where we can control important parameters like data size and its distribution precisely. These benchmarks were central to the success and adoption of database management systems. But more and more, data management problems are of a semantic nature. An important example is finding tables that can be unioned. While any two tables with the same cardinality can be unioned, table union search is the problem of finding tables whose union is semantically coherent. Semantic problems cannot be benchmarked using synthetic data. Our current methods for creating benchmarks involve the manual curation and labeling of real data. These methods are not robust or scalable and perhaps more importantly, it is not clear how robust the created benchmarks are. We propose to use generative AI models to create structured data benchmarks for table union search. We present a novel method for using generative models to create tables with specified properties. Using this method, we create a new benchmark containing pairs of tables that are both unionable and non-unionable but related. We thoroughly evaluate recent existing table union search methods over existing benchmarks and our new benchmark. We also present and evaluate a new table search methods based on recent large language models over all benchmarks. We show that the new benchmark is more challenging for all methods than hand-curated benchmarks, specifically, the top-performing method achieves a Mean Average Precision of around 60%, over 30% less than its performance on existing manually created benchmarks. We examine why this is the case and show that the new benchmark permits more detailed analysis of methods, including a study of both false positives and false negatives that were not possible with existing benchmarks

Polyaluminum chloride coagulation in drinking water treatment

Dissolved organic matter, DOC [mg C/L], is a representative parameter for the content of organic matter in natural waters. Along the A254 absorbance as a measure of organic compounds determined at X = 254nm UV [cm'1], DOC underlies the calculation of SUVA (Specific Ultraviolet Absorbance) = A254/DOC x 100 [L-m '-mg'1]. SUVA can be used to describe the composition of the water in terms of hydrophobic / hydrophilic character. The study results are presented in the coagulation process with pre-hydrolyzed aluminum salt, as a polyaluminum chloride (PAC1), a simple salt of Al, as an A1 sulfate (Alum), applied to surface water intended for drinking water. From SUVA values DOC removal efficiency in the coagulation process is estimated. SUVA = 2-4 estimated efficiencies of 25-50% removal in DOC. When using PAC1, DOC removal efficiency is within the range of 27-53%, and slightly lower when using Alum. SUVA values <2 indicate DOC removal efficiencies <25%. DOC removal efficiencies obtained from the use of Alum are within the range 5.6-8.4% and those obtained when used as coagulation agent PAC1 are in the range 20.8-23.8%

Perbandingan efisiensi adsorben fly ash dan dolomit yang berasal dari Sumatera Barat terhadap penyerapan methylene blue

Fly ash  merupakan limbah hasil pembakaran pabrik kelapa sawit yang mengandung silika dan mineral alkali yang dikenal sebagai komponen penting adsorben. Dolomit diketahui juga sebagai material sedimentasi karbonat yang memiliki kemampuan menyerap logam. Bahan baku utama yang digunakan untuk penelitian adalah fly ash  yang diperoleh dari Pabrik Kelapa Sawit Mutiara Agam di Provinsi Sumatera Barat, dolomit dari Kamang, dan methylene blue (MB) sebagai senyawa model limbah tekstil. Bahan tersebut dikalsinasi 900oC selama 4 jam, kemudian dihaluskan dan diayak agar ukuran partikel seragam. Adsorben dolomit dan fly ash dilakukan karakterisasi morfologi dan dilanjutkan dengan uji penyerapan pada larutan MB. Berdasarkan hasil uji penyerapan, adsorben fly ash memiliki efisiensi penyerapan tertinggi sebesar 99,236% dibandingkan dengan adsorben dolomit yaitu sebesar 46,9%

A Model for an Intelligent Support Decision System in Aquaculture

The paper purpose an intelligent software system agents–based to support decision in aquculture and the approach of fish diagnosis with informatics methods, techniques and solutions. A major purpose is to develop new methods and techniques for quick fish diagnosis, treatment and prophyilaxis at infectious and parasite-based known disorders, that may occur at fishes raised in high density in intensive raising systems. But, the goal of this paper is to presents a model of an intelligent agents-based diagnosis method will be developed for a support decision system