FIVE SPECIES OF PSELAPHINAE (COLEOPTERA: STAPHYLINIDAE) NEW FOR THE FAUNA OF ALBANIA

The Large High Altitude Air Shower Observatory (LHAASO) project is proposed to study high energy gamma ray astronomy and cosmic ray physics. As a component of the project, the wide field of view Cherenkov telescope array is designed to study the spectrum and compositions of cosmic rays. Two prototype telescopes have been operated since 2008. The weather condition and the air quality are very crucial to the energy reconstruction and spectrum calculation. So in order to calibrate the weather and air condition, the UV bright stars are used, as a cross check, and the infrared data are also analyzed.

<p>The aim of this project is to investigate the influence of health-relevant air-hygienic and climatological parameters on emergency room visits at the University Hospital of Augsburg. This is achieved by quantifying the effects of increased exposure to air substances and weather extremes.</p><p>The emergency admission data from 2017 and 2018 are available as medical data basis. Among the air constituents, ozone, nitrogen dioxide and particulate matter values ​​are used, measured by several stations in Augsburg. The meteorological database contains the daily mean and daily maximum values ​​of several parameters such as air temperature, humidity, and wind. In addition, a catalog of different weather conditions was created. For this purpose, five Principal Component Analyzes were performed, one for each season and one for the entire year.</p><p>The medical data set was broken down according to the seasons and days with specific meteorological conditions (e. g. heat days) or days that exceeded the WHO recommendations for air pollutants. These sub-divisions were undertaken in order to identify differences of the number of admissions under the occurrence of extreme days. To account for the possible delay between exposure and emergency cases, a lead time of up to seven days was included.</p><p>The results so far show that in almost all subgroups of the ICD-10 classification there are highly significant correlations between the weather and air conditions and the number of emergency admissions. These occur almost exclusively on meteorological extreme days or when the WHO air pollution guideline values are exceeded. The strength of the correlations between the individual diagnoses and subgroups differs significantly. The proportion of age, gender and place of residence-specific differences can be classified as low. However, there are individual diseases in almost every main group that are showing very clear differences, sometimes even opposite correlations, between men and women or urban and suburban residents.</p><p>The project is funded by the German Federal Environment Foundation and the German Research Foundation under - project number 408057478.</p>

A comfortable indoor thermal environment is usually controlled through air conditioning, which can greatly improve work efficiency. However, current air conditioning system design may not consider the spatio-temporal distribution of the indoor environment for large-scale buildings, so the air conditioning system needs to be optimized. Few studies have examined such optimization related to different weather conditions outside the building. This study presents a method to formulate an air condition adjustment strategy for each specific zone in a library with large glass curtain walls, based on the diurnal change of thermal environment. We measured the indoor thermal environmental conditions and recorded the thermal comfort perception of room occupants. The Predicted Mean Vote (PMV) and apparent temperature model were adopted to predict the occupants’ thermal comfort, resulting in a suggested comfort range. Based on our results, the comfortable range of PMV and apparent temperature was identified, and this comfortable range could be used as a basis for an example of adjusting the air conditioning system to improve indoor thermal comfort.

In offshore platforms, with high well density, slot recovery technique is an efficient way to target new / un-swept avenues to boost the production levels in a mature field. This leads to utilization of an appreciable length of parent bore which is an advantage to the operators globally in terms of surface facility retention and associated rig time saved. This paper discusses an actual case-study wherein dual casing exit was achieved in an offshore platform well resulting in significant time and cost savings. For the subject well the subsurface targets were quite far from the mother-bore, resulting in a plan to side-track the well at a shallow depth where double casing existed, i.e. 9-5/8″ × 13-3/8″. The options available were pilot milling and dual exit using whipstock. Unlike multi-casing exits, pilot milling is a time consuming method which requires multiple trips and involves large volume of metal swarf handling at surface. The CBL-VDL verified the presence of cement outside 9 5/8″ casing that further supported the case of dual casing exit operation. Consequently, associated risks were discussed and plans to mitigate the same were put in place. Single-trip 8-1/2″ whipstock-milling system was used to cut a window suitable for running drilling BHAs, liner, and completion equipment. The 9-5/8″ × 13-3/8″ annulus was monitored during milling and FIT test to check for any pressure communications. For well control scenario, arrangements were made for connecting the annulus to the choke manifold to ensure a closed system and thereby have provision of circulating through choke in case of gas migration in the 9-5/8″ × 13-3/8″ annulus. The window milling operation was done using sea water & intermittent Hi-vis sweeps. The window was milled successfully in a "single trip", thereby saving considerable rig time. No excess drag or held-up was observed and gauge loss on mills when pulled out of the hole was negligible. Well integrity was intact with no pressure communication in the annulus. The job was a successful one that led to finishing the well within the planned time and thereby, led to timely release of the jack up rig before the onset of adverse weather conditions. Multi-casing exit technology in two or three casing strings opens the multi-level advantages to well intervention techniques especially in situations where the wells are old with limited access due to presence of fish or other restrictions that makes the deeper section of the well non-usable. Such sections can be avoided by sidetracking at a shallow depth and also provides an opportunity to access targets that are quite far from the original mother-bore.

All drivers have their own habitual choice of driving behavior, causing variations in fuel consumption. It would be beneficial to classify these driving styles and extract the most economical and ecological driving patterns. However, driving style of each driver is not consistent and may vary within a single trip. Therefore, this paper proposes a novel technique to robustly classify driving style using the Support Vector Clustering approach, which attempts to differentiate the variations in individual's driving pattern and provides an objective driver classification. Real driving data of three human participants were collected using an instrumented vehicle. For data processing, each trip data were first segmented into separate event groups. Prominent factors were then extracted by applying Principal Component Analysis on both statistical and spectral features of all signals. Afterwards, Support Vector Clustering was performed to classify driving style during the trip. The trained classifier was used to indicate the driving pattern variations in percentage. The validity of the proposed method was evaluated using the jerk profile, where a high correlation was found between the classification results and jerk distributions. Moreover, a positive relation between fuel consumption and driving aggressivity was also confirmed. Furthermore, it was found that weather condition, time of the day and ultimately, the driver's eagerness, can cause significant variations in driving style.

Weather conditions is an air condition in a place with a relatively short time, which is expressed by the value of parameters such as temperature, wind speed, pressure, rainfall, which is another atmospheric phenomenon as the main component. Human activities can be influenced by weather conditions, such as transportation, agriculture, plantation, construction or even sports. Therefore, for determining the weather, getting weather information needs to be made so that it can be utilized by the community. Problems that arise how to make weather predictions automatically so that it can be done by everyone. In this study proposed several algorithms Navie Bayes, Decision Tree, Random Forest to calculate the opportunities of one class from each of the existing group attributes and determine which class is the most optimal, meaning that grouping can be done based on the categories that users enter in the application. The prediction system has been made to obtain an accuracy rate of Navie Bayes of 77.22% with a standard deviation of 29%, a Decision Tree accuracy rate of 79.46% with a standard deviation of 15%, a random forest accuracy rate of 82.38% with a standard deviation of 43%.

Hospitals are large buildings that consume a great amount of energy mostly due to their continuous energy consumption needs, high consumer medical equipment, and special requirements of thermal and air conditions. Reliable dynamic simulation is a chimera because of the complex design and behavior of these buildings. Therefore, monitoring-based methods arise as a plausible alternative. Its main drawback, however, is the lack of enough data to generate statistically robust models. The paper faces this problem thanks to the helpful contribution of a collaborative hospital which was able to generate daily data of electrical energy consumption for a period of six years. Besides, thirteen variables that summarize the daily activity of the hospital are also included. The results show how machine learning techniques generate models that accurately predict the electrical energy consumption based on weather conditions and activity measurements. The obtained results are useful for the design of more specific energy saving strategies, a more efficient economic investment for energy retrofitting of existing buildings and a better management of economic energy cost in large-scale buildings.

Meeting thermal comfort in buildings with high latent loads requires the treatment of the air humidity with specific technologies. Desiccant based systems have proved to be effective in removing ventilation latent load in heating-ventilation and air conditioning systems. Typically, desiccant based systems are part of the air-handling units, which control both temperature and humidity. Based on the above, this paper presents an innovative solution designed to be integrated in the building envelope. It consists of a honeycomb desiccant block placed inside a ventilated façade. The regeneration of the desiccant material is carried out by a solar air collector, which is also integrated in the façade. The aim of the system is to dehumidify the ventilation air before entering the air-handling unit using the façade as an active element of the system. The solution is built at a pilot scale and tested under Mediterranean climate conditions. The experimental tests lead to a numerical model implemented in TRNSYS. This model is used to evaluate the performance of the solution in an office building comparing with a conventional cooling coil system, the conventional alternative. Results show that, under Mediterranean climate, the desiccant façade ensures a humidity comfort greater than 92% in neutral supply air conditions (26 °C and 50%). Under the same neutral conditions, conventional systems can only achieve this level of comfort by supplying colder air in order to force the condensation of the air in the cooling coil, which entails an energy consumption increase of 29%. Last, results confirm that the desiccant façade leads to lower energy consumption in both Mediterranean and Subtropical climates while conventional systems are more appropriate in cold semi-arid climates.

The prosperous implementation of Building Integrated Photovoltaics (BIPV), as well as Building Attached Photovoltaics (BAPV), needs an accurate and detailed assessment of the potential of solar irradiation and electricity production of various commercialised technologies in different orientations on the outer skins of the building. This article presents a dataset for the solar incident radiation and electricity production of PV systems in the north and south orientations in a dense urban area (in the northern hemisphere). The solar incident radiation and the electricity production of two back-to-back PV panels with a ten-centimetre gap for one year are monitored and logged as primary data sources. Using Microsoft Excel, both panels’ efficiency is also presented as a secondary source of data. The implemented PV panels are composed of polycrystalline silicon cells with an efficiency of 16.9%. The results depicted that the actual efficiency of the south-facing panel (13–15%) is always closer to the standard efficiency of the panel compared to the actual efficiency of the north-facing panel (8–12%). Moreover, although the efficiency of the south-facing panel on sunny days of the year is almost constant, the efficiency of the north-facing panel decreases significantly in winter. This phenomenon might be linked to the spectral response of the polycrystalline silicon cells and different incident solar radiation spectrum on the panels. While the monitored data cover the radiation and system electricity production in various air conditions, the analysis is mainly conducted for sunny days, and more investigation is needed to analyse the system performance in other weather conditions (like cloudy and overcast skies). The presented database could be used to analyse the performance of polycrystalline silicon PV panels and their operational efficiency in a dense urban area and for different orientations.

Objective: This study aimed to explore the impact of environmental factors, specifically adverse weather conditions, on the rate of pediatric emergency department visits. Methods: A retrospective analysis was performed at a tertiary care hospital specializing in pediatric and emergency medicine. Data on environmental factors, including particulate matter (PM10) and nitrogen oxide (NOX) levels, temperature, wind speed, and precipitation, were collected from official Turkish sources and correlated with pediatric emergency department admissions over a year. Admissions were categorized seasonally, and analyzed variables included patient demographics, visit details, diagnosis, and outcomes, utilizing ICD-10 codes for a broad range of conditions. Results: A retrospective study from 2019 to 2022 investigated 126,364 pediatric patients aged < 15 years. Seasonal variation was noted, with the highest number of admissions in the fall of 2022. Admissions significantly decreased during periods of water and air pollution, with the most substantial decline seen during the pandemic year of 2021. During the post-pandemic period, there was a sharp increase in the number of visits. Upper respiratory tract infections (URTIs) constitute 47% of all cases and are the leading cause of emergency department visits. The majority of patients (99%) walked, whereas only 1% arrived by ambulance. (0.2%) required referral to higher-level care, and 3% of emergency visits resulted in hospital admissions, of which 5% required intensive care. The frequency of visits was correlated with environmental factors, with fewer visits occurring during adverse weather conditions (P<.001). URTIs remained the predominant diagnosis, even during extreme weather or pollution events, suggesting that such environmental factors did not diminish the urgency of these conditions. Conclusion: This study found that environmental factors influence child emergency room visits, particularly during summer when children engage in more outdoor activities due to warmer weather. Based on the results obtained, health systems are able to allocate resources and formulate response strategies that enhance the provision of care for children, thereby increasing the resilience of the emergency health services infrastructure.

The review focuses on the progresses of air microbes in indoor environment, including their sources, sorts, influence of human body health, spread regulation and spatiotemporal distribution characteristics. Most airborne microorganisms attached to the dust particles, which is present in the air in the form of aerosol .The distribution of microbe is related with indoor environment condition in the air. The factor which influences it to distribute mainly includes temperature, degree of humidity, wind velocity and weather condition etc. Air microbe is bred easily in ventilation and air conditioning system. The growth of microbe has a tremendous relation with the ventilation and air condition. The distribution of microorganisms in different regions, different buildings and different ventilation system is different from each other.

Weather conditions is an air condition in a place with a relatively short time, which is expressed by the value of parameters such as temperature, wind speed, pressure, rainfall, which is another atmospheric phenomenon as the main component. Human activities can be influenced by weather conditions, such as transportation, agriculture, plantation, construction or even sports. Therefore, for determining the weather, getting weather information needs to be made so that it can be utilized by the community. Problems that arise how to make weather predictions automatically so that it can be done by everyone. In this study proposed several algorithms Navie Bayes, Decision Tree, Random Forest to calculate the opportunities of one class from each of the existing group attributes and determine which class is the most optimal, meaning that grouping can be done based on the categories that users enter in the application. The prediction system has been made to obtain an accuracy rate of Navie Bayes of 77.22% with a standard deviation of 29%, a Decision Tree accuracy rate of 79.46% with a standard deviation of 15%, a random forest accuracy rate of 82.38% with a standard deviation of 43%.

This study aims to analyze the vertical distribution of aerosol particles during autumn and winter by deriving the number density of aerosol particles from Mie Lidar return signals. Haze has always existed in regions with low humidity and stable weather. In such regions,particle vertical distribution characters are needed for transport mechanisms. As a widely used instrument, Mie Lidar surfer from the uncertainties caused by the Lidar ratio and boundary value assumption. However, Mie Lidar is the most powerful tool to observe the atmospheric vertical distribution because it performs well in day and night under all weather conditions in comparison with passive sensors.Moreover, Mie Lidar is easier to carry compared with Raman Lidar.Lidar radiation principles were studied to understand the Lidar equation and its solution. Several parameters were carefully chosen for the numerical solutions. Instead of using boundary value for approximation, the Lidar parameter k was directly calculated on the basis of Lidar-validated parameters to avoid uncertainties. Thus, aerosol particles backscatter and extinction coefficient were retrieved by solving the Lidar equation and integrated for Aerosol Optical Depth(AOD) results. A Cimel CE318 sun photometer and an ASD Field Spec3 were used to measure AOD as validation data. Lastly, the number density of aerosol particles was derived on the basis of Mie theory with the estimation of aerosol particle distribution, size, and refractive index of particles.According to the number density vertical profile, nearly no difference was observed between the number density vertical profile of aerosol particles during daytime and nighttime in one day at a low altitude. This result indicated that the sunlight effect is ignorable for aerosol particle distribution near ground. The retrieved AODs with the CE318 and ASD observations showed a root mean square error of approximately 0.0541 and 0.0100, demonstrating that the retrieved result is reliable. The comparison with traditional method showed that the proposed algorithm in this study improved the accuracy of the traditional Lidar equation solution. The near-real distribution results showed aerosol particle distribution characteristics near the ground. The number density of aerosol particles in Guangzhou was mainly distributed in low-altitude regions, and its relationship with height was approximately a negative exponential distribution. The retrieved results of the aerosol particle number density were similar to those from the previous years, indicating that the air pollution in Guangzhou has not worsened. However, potential environmental threats and air pollution problems still need to be governed.Several uncertainties, such as changes in air temperature and pressure, validation of Lidar instrument k, and overlap factor, that could cause deviation were identified. The overlap factor and molecular backscatter coefficient were carefully calculated using the theoretical method. Frequent updates on Lidar parameters will be requested from the Lidar company in the future. The theoretical and experimental methods were combined for the calculation of the overlap factor and molecular backscatter coefficient. Raman Lidar was used to measure the actual Lidar ratios and characteristics of molecular scatter.The aerosol particle number density-retrieved algorithm was based on Lidar radiation principles and revealed the particles’ spatial distribution characters. The vertical distribution characters remained unchanged for several days under stable weather in the autumn and winter seasons, and its relationship with height was approximately a negative exponential distribution;hence, air conditions can have huge effect on human health. Air pollution problem still needs to be governed.

Weather is an air condition in the atmosphere layer somewhere. There are many human activities that depend on weather conditions. Based on this, weather forecasting is able to solve problems that occur around. One of the human activities that is very influential on climate change is flight activities at Juanda Airport in Surabaya. Where all activities that occur in aircraft flight are determined based on weather conditions observing surface air observations which are then sent to the analysis and weather forecasting section. The process of determining weather forecasts with manual observation methods often occur errors. Based on this, research is conducted using learning methods based on weather history data in the hope that it can provide weather forecasts that are more efficient and have a higher level of accuracy. The method used is semi supervised learning using recursive k-means. Semi supervised learning uses recursive k-means is a method of development of the k-means clustering method. This study uses five weather variables: Minimum temperature, maximum temperature, average temperature, humidity and duration of solar radiation. the best learning process is done with a threshold value of ten which results in an accuracy rate of 100%. And to make it easier for users to use this system, this research also creates an interface system that can be used by users.

The energy consumption for heating and cooling in the building sector accounts for more than one-third of total energy used worldwide. In view of that, it is important to develop energy efficient cooling and heating systems in order to conserve energy in buildings as well as reduce greenhouse gas emissions. In both commercial and residential buildings, the heat pump has been adopted as an energy efficient technology for space heating and cooling purposes as compared to conventional air conditioning systems. However, heat pumps undergo standard testing, rating, and certification procedures to ascertain their system performance. Essentially, the calorimeter for testing heat pumps has two test chambers to serve as a heat source and heat sink to control and maintain the test conditions required to simulate the heat pump indoor and outdoor units, simultaneously. In air-to-air heat pump units, the conventional calorimeter controls the air temperature and humidity conditions in each test chamber with separate air handling units consisting of a refrigerator, heater, humidifier, and supply fan, which results in high energy consumption. In this study, using dynamic modeling and simulation, a new calorimeter for controlling air conditions in each test chamber is proposed. The performance analysis based on simulation results showed that the newly proposed calorimeter predicted at least 43% energy savings with the use of a heat recovery unit and small refrigerator capacity as compared to the conventional calorimeter that utilized a large refrigerator capacity for all the weather conditions and load capacities that we investigated.