Metal iodide materials as novel components of thermal biological and medical systems at the interface between heat transfer techniques and therapeutic systems. Due to their outstanding heat transfer coefficients, biocompatibility, and thermally activated sensitivity, metal iodides like silver iodide (AgI), copper iodide (CuI), and cesium iodide (CsI) are considered to be useful in improving the performance of medical instruments, thermal treatment processes, and diagnostics. They are examined for their prospective applications in controlling thermal activity, local heating therapy, and smart temperature-sensitive drug carrier systems. In particular, their application in hyperthermia therapy for cancer treatment, infrared thermal imaging for diagnosis, and nano-based drug carriers points to a place for them in precision medicine. But issues of stability of materials used, biocompatibility, and control of heat—an essential factor that would give the tools the maximum clinical value—remain a challenge. The present mini-review outlines the emerging area of metal iodides and their applications in medical technologies, with a special focus on the pivotal role of these materials in enhancing non-invasive, efficient, and personalized medicine. Over time, metal iodide-based systems scouted a new era of thermal therapies and diagnostic instrumentation along with biomedical science as a whole.

Fraudulence in cosmetic ingredients is becoming increasingly prevalent, alongside the rising demand and utilization of cosmetics within the populace. One of the whitening agents still utilized in cosmetics is mercury, present in forms such as mercury chloramide (HgNH₂Cl₂) and mercury chloride (HgCl₂). Prolonged mercury exposure can have adverse health effects. To address this issue, alternative mercury analysis methods in samples have been developed, including the utilization of silver nanoparticles amalgamated with sweet potato starch as a stabilizing agent. This paper aims to delve into the roles of silver nanoparticle AgNO₃ and sweet potato starch (as a stabilizer) as a sensor for mercury detection, which can be applied in cosmetic products. Detection of mercury utilizing nanoparticles is based on the Surface Plasmon Resonance phenomenon, which endows a high level of selectivity and sensitivity toward the presence of mercury metal ions. When interaction occurs between mercury metal and silver nanoparticles, the liquid undergoes a color change from yellowish-brown to transparent. This phenomenon arises from the oxidation of AgO (yellow) to Ag⁺ ions (transparent) by the mercury metal. Consequently, a silver nanoparticle sensor utilizing sweet potato starch as a stabilizing agent exhibits the potential to detect mercury metal within a substance with high efficacy.

Metal organic framework is a class of hybrid network of supramolecular solid materials comprised of a large number of inorganic and organic linkers all bounded to metal ions in a well-organized fashion. This type of compounds possess a greater surface area with an advantage of changing pore sizes, diversified and beautiful structure which withdrew an intense interest in this field. In the present review articles, the structural aspects, classification, methods of synthesis, various factors affecting the synthesis and stability, properties and applications have been discussed. Recent advances in the field and new directions to explore the future scope and applications of MOFs have been incorporated in this article to provide current status of the field.

Identify and diagnosis of homogenous units and separating them and eventually planning separately for each unit are considered the most principled way to manage units of forests and creating these trustable maps of forest’s types, plays important role in making optimum decisions for managing forest ecosystems in wide areas. Field method of circulation forest and Parcel explore to determine type of forest require to spend cost and much time. In recent years, providing these maps by using digital classification of remote sensing’s data has been noticed. The important tip to create these units is scale of map. To manage more accurate, it needs larger scale and more accurate maps. Purpose of this research is comparing observed classification of methods to recognize and determine type of forest by using data of Land Cover of Modis satellite with 1 kilometer resolution and on images of OLI sensor of LANDSAT satellite with 30 kilometers resolution by using vegetation indicators and also timely PCA and to create larger scale, better and more accurate resolution maps of homogenous units of forest. Eventually by using of verification, the best method was obtained to classify forest in Golestan province’s forest located on north-east of country.

Every year, hundreds of fires occur in the forests and rangelands across the world and damage thousands hectare of trees, shrubs, and plants which cause environmental and economic damages. This study aims to establish a real time forest fire alert system for better forest management and monitoring in Golestan Province. In this study, in order to prepare fire hazard maps, the required layers were produced based on fire data in Golestan forests and MODIS sensor data. At first, the natural fire data was divided into two categories of training and test samples randomly. Then, the vegetation moisture stresses and greenness were considered using six indexes of NDVI, MSI, WDVI, OSAVI, GVMI and NDWI in natural fire area of training category on the day before fire occurrence and a long period of 15 years, and the risk threshold of the parameters was considered in addition to selecting the best spectral index of vegetation. Finally, the model output was validated for fire occurrences of the test category. The results showed the possibility of prediction of fire site before occurrence of fire with more than 80 percent accuracy.