The Development and Potential of a Digital out of Home Food Environment Monitoring Platform.

The rapidly growing field of digital meal delivery platforms has transformed the out of home (OOH) food environment, presenting both opportunities and challenges for public health. This paper introduces the development and potential of a novel digital platform designed for monitoring the OOH food environment. Drawing on publicly available data from meal delivery applications, this platform provides valuable insights into the landscape of digital food offerings, such as the most common restaurants per region, average caloric content per meal type, and energy value per monetary unit. This research addresses the current void in regulations for this digital environment, particularly around food labeling and provision of nutrition information. Even though the platform has significantly improved our understanding of the digital food ecosystem, it highlights gaps, primarily due to the lack of publicly available individual data and inconsistencies in provided information. Despite these challenges, the proposed digital platform holds considerable promise for better understanding the digital food environment, supporting healthier food choices, and informing future policy interventions aimed at regulating the online food environment. This research advocates for mandatory regulations in the digital food sector to ensure comprehensive, comparable, and transparent nutrition information and equality in access to nutritious foods.

Soft Sensing of LPG Processes Using Deep Learning.

This study investigates the integration of soft sensors and deep learning in the oil-refinery industry to improve monitoring efficiency and predictive accuracy in complex industrial processes, particularly de-ethanization and debutanization. Soft sensor models were developed to estimate critical variables such as the C2 and C5 contents in liquefied petroleum gas (LPG) after distillation and the energy consumption of distillation columns. The refinery's LPG purification process relies on periodic sampling and laboratory analysis to maintain product specifications. The models were tested using data from actual refinery operations, addressing challenges such as scalability and handling dirty data. Two deep learning models, an artificial neural network (ANN) soft sensor model and an ensemble random forest regressor (RFR) model, were developed. This study emphasizes model interpretability and the potential for real-time updating or online learning. The study also proposes a comprehensive, iterative solution for predicting and optimizing component concentrations within a dual-column distillation system, highlighting its high applicability and potential for replication in similar industrial scenarios.

Bladder cancer: a review of non-muscle invasive disease.

BACKGROUND: Bladder cancer is one of the most common cancers affecting men and women and thus has a profound impact on health care. The majority of patients (75%) with newly diagnosed urothelial tumors have non-muscle invasive disease confined to the bladder mucosa or the lamina propria. METHODS: The authors review the literature as well as recently published clinical guidelines regarding the bladder cancer risk and causative factors, diagnostic and pathologic evaluation, prognostic variables, and management strategies for patients with non-muscle invasive bladder cancer. RESULTS: Recurrence and progression remain problematic for many patients and are dependent on multiple clinical and pathological features, the most important of which are tumor stage, grade, multifocality, size, recurrence patterns, and the association with carcinoma in situ. Accurate assessment of clinical stage and tumor grade is critical in determining management and surveillance strategies. Intravesical therapies positively influence tumor recurrence rates. Disease progression rates may be impacted in high-risk patients who receive both induction bacille Calmette-Guérin (BCG) and a maintenance BCG regimen. Cystectomy still plays a pivotal role in patients with high-risk tumors and in patients who fail more conservative attempts to eradicate non-muscle invasive disease. CONCLUSIONS: Non-muscle invasive bladder cancers represent a broad group of tumors with varying biologic potential. Successful treatment depends on the careful integration of diagnostic and surveillance tests, macroablation through transurethral resection, accurate assessment of clinical stage, and the timely and appropriate delivery of intravesical chemotherapeutic and immunomodulatory agents.

Laparoscopic retroperitoneal lymph node dissection in the management of testis cancer.

BACKGROUND: The surgical approach to management of testis cancer has been traditionally through an open incision, but in the last decade, several centers have reported their experience with laparoscopic retroperitoneal lymph node dissection (LRPLND). METHODS: We reviewed the English literature, summarized the outcomes, and included our initial experience with the LRPLND procedure. RESULTS: Improvements in operative time, complications, and morbidity have occurred as surgical experience has increased. The procedure is more challenging in postchemotherapy patients. Outcomes at our institute are comparable to reported series from other institutions, and LRPLND is our current procedure of choice for RPLND. CONCLUSIONS: LRPLND has been shown to be a safe, effective, minimally invasive procedure in the management of testicular cancer patients who require surgery to address the retroperitoneal lymph nodes.