As one of the typical deposits in Sichuan-Yunnan-Guizhou Pb-Zn metallogenic province, the Daliangzi Pb-Zn deposit has a close genetic relation with the structural system of “black/fracture zone” formed under the action of NWW-approx. EW strike-slip structures in the metallogenic province, but the mechanism of rock and ore-controlling action of the structural system remains unclear. Based on detailed analysis of tectonite-mineralized alteration lithofacies of R1 “black/fracture zone”, the tectonite-mineralized alteration lithofacies zones can be divided into 4 types in succession outward from the Pb-Zn mineralization center, i.e., ① brecciated and stockwork-like Pb-Zn mineralization-complex breccia facies zone, ② stockwork-like Pb-Zn mineralization-simple breccia and cataclasite facies zone, ③ veined pyrite-sulfide-dolomitic cataclasite facies zone, and ④ fine-veined calcite-black carbonized dolomite facies zone; Cl- and SO42--rich high-temperature oxidizing ore-bearing fluid mixes with reducing fluid in Zone ① while migration and precipitation occur in Zone ④. With the evolution of ore-forming fluid, the homogenization temperature decreases from Zone ① to Zone ④, the salinity increases from Zone ① to Zone ② and then decreases to Zones ③ and ④, and the fluid density has little change on the whole; the contents of Zn, Pb, Cu, Ga, Ge, Cd, Ag, and other metallogenic elements, the Zn/Pb ratio, and the CaO/MgO mole ratio decrease gradually from Zone ① to Zone ④; and REE fractionation, calcilization, silicification, and pyritization enhance gradually from Zone ① to Zone ④. This series of changes are the product of diapirism (cryptoexplosion) of strike-slip structures and “black/fracture zone”, among which the second-order structures (F5, F100, and other faults) derived from NWW-approx. EW striking dextral shear-tension faults F1 and F15 control the brecciated and stockwork-like Pb-Zn mineralized complex breccia facies zones and the stockwork-like Pb-Zn mineralized simple breccia and cataclasite facies zones. Therefore, this paper establishes the zoning mode of tectonite-mineralized alteration lithofacies of “black/fracture zone” and proposes that Zones ① and ② are important prospecting criteria. conclusions.

Present study on land use land cover analysis and taluka wise combination of cropped area, carried out in Beed district of Maharashtra state in India and studied 11 talukas of the district. Secondary data on land use land cover and area in various crops has been collected, compiled and analyzed from various secondary sources. Simple descriptive statistics used to examine the agricultural land use land cover and J.C. Weaver’s crop combination technique was used for taluka wise analysis of crop combinations and on the basis of highest to smallest area engaged in particular crop, crops have been ranked. The study showed that, in the district as a whole, percentage of uncultivated land other than fallow was about 46 percent as compare to net sown area is only 35 percent in total geographical area (1063855ha) of Beed district. The study further revealed that about 42 percent area engaged in food crops and about 58 percent area occupied by nonfood crops, which includes cereals (25%), cotton (19%), pulses (13%), oilseed (12%), sugarcane (5%) and fruits and vegetables (3%). Results of Weaver’s crop combination technique showed that the Wadwani taluka demarcated as most diversified taluka by means of maximum number (sixteen) of crops combinations with the lowest variance value (14) found in the present investigation. Further, the study revealed that the high crop diversity found in Dharur taluka with eighteen crop combinations and the taluka has given first rank amongst others. To derive the crop diversity ranking, taluka wise maximum to minimum number of crop combinations derived in different talukas and given ranks accordingly. The study concluded that, agricultural land use analysis is essential for solving and optimizing the land use problem, maintaining socio-ecological balance, best utilization of scarce resources. The authors enlightened that the rational use of agricultural land has important for food security and biodiversity which increase the productivity and profitability of the agriculture sector and it indicates the decision making ability of farmers in study area.

Marine sand, in addition to oil and gas resources, is the second largest marine mineral resource. The rational development and utilization of marine sand resources are conducive to the growth of the marine economy. Marine sand can be categorized into surface marine sand and buried marine sand. Buried marine sand deposits are buried beneath the sea floor, making it challenging to locate them due to their thin distribution. Consequently, there exist numerous technical difficulties associated with marine sand exploration. In this study, various geophysical methods such as sub-bottom profile survey and single-channel seismic survey were employed in the northern offshore waters of the South China Sea along with offshore engineering geological drilling. As a result, two distinct marine sand bodies were delineated within the study area. Additionally, five reflective interfaces (R0, R1, R2, R3 and R4) were identified from top to bottom. These interfaces can be divided into five seismic sequences: A2, B2, C2, D2 and E2 respectively. Three sets of strata were recognized: the Quaternary Holocene Marine sedimentary layer (Q4m), the Upper Pleistocene alluvial layer, and the diluvial sedimentary layer (Q3al+pl) as well as the Upper Pleistocene Marine sedimentary layer (Q3m). Sequence A2 and B2 corresponded to Quaternary Holocene Formation (Q4m) within the study area while sequence C2 corresponded to B2 as well as upper Pleistocene alluvial and alluvial deposits (Q3al+pl), which represent one of the main occurring layers of sand in this region. Lastly, E2 represents a layer composed mainly of silty clay and round gravel known as upper Pleistocene marine sediment layer (Q3m). In total, two ore bodies containing marine sand have been discovered during this study. The estimated total resource amounts approximately 46 million m³ with an average thickness around 11 meters.

The risk classification of barrier lake is the key to conducting emergency treatment in a scientific manner. Such difficulties are faced as short time for risk evaluation, complex evaluation indicators, difficulty in obtaining information quickly and quantifying index weights. Based on this, this paper constructs a quantitative risk classification model for barrier lakes based on D-AHP. On the basis of studies on nearly 100 cases of barrier lakes, an 8-factor evaluation index system and quantitative classification are proposed. The methods of rapid calculation of reservoir capacity curve of barrier lake and intelligent identification of particles on the surface of barrier body were developed, which realized the rapid acquisition of 8-factor evaluation index information in emergency environment. The D-AHP method dealt with inconsistent weight assignment to evaluation factors by experts, which helped achieve weight quantification of 8 factors. The risk assessment on15 barrier lakes such as Tangjiashan barrier lake show that the conclusion drawn with risk classification method proposed in this paper is basically consistent with that of the traditional table-lookup method. However, the table-lookup method ignores cumulative loss’s impacts on the risk level of barrier lakes, and considers the extremely severe loss of barrier lakes as a sufficient condition for the evaluation level to be grade I, and thus a deviation in the evaluation. The risk classification method proposed in this paper is more reasonable and reliable.

To study the spatial accessibility of healthcare services in cities on the Tibetan Plateau, this paper proposes an improved 3SFCA method in the main urban area of Xining City. The improved 3SFCA method incorporates both selection weights from the Huff model and the M2SFCA approach to addressing suboptimal allocation of supply resources. Results show that the accessibility of healthcare for the main urban area of Xining City decreases in a circle to four axes, with good accessibility in the east and center but poor accessibility in the north. And there is a positive spatial relationship between population and accessibility. Besides, this paper proposes a framework for studying the spatial accessibility of healthcare on the Tibetan Plateau.

Soil potentially toxic elements (PTEs) pollution in black soil is of significant concern in China. However, research is scarce regarding the current status, ecological risk, and human health risk of PTEs in the black soil area of northeast China. In our study, 304 soil samples (0-20 cm) were collected in Gonghe Town, Hailun City. The pollution degree and spatial distribution patterns of soil PTEs were analyzed by using the single-factor pollution index (PI) and the Nemerow pollution index (NPI). The source apportionment of PTEs was carried out by combining correlation analysis, principal component analysis (PCA), and positive matrix factorization (PMF) methods. Meanwhile, the potential ecological risk (RI) and the human health risk (HI) associated with soil contamination from various sources were evaluated through the RI and the HI. The results demonstrated that the average content (mean ± standard deviation) of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn were 11.16 ± 1.32 mg/kg, 0.11 ± 0.04 mg/kg, 65.29 ± 3.46 mg/kg, 22.56 ± 1.32 mg/kg, 0.03 ± 0.01 mg/kg, 27.07 ± 1.46 mg/kg, 26.09 ± 2.84 mg/kg, and 66.01 ± 4.52 mg/kg, respectively. The overall PTEs in the study area exhibited slight pollution levels. The PCA and PMF model source apportionment was validated against each other to yield four sources, which were natural source (33.2%), irrigation source (29.5%), traffic source (23.4%), and fertilizer source (13.2%). The overall RI in the study area was determined to be at the level of slight ecological risk. The non-carcinogenic risk of PTEs to children and adults was ignored, and the carcinogenic risk was at an acceptable level. The comprehensive analysis of PTEs, pollutant sources, RI, and HI concluded that the fertilizer source should be the primary control source, with Cd identified as the first control PTE. The irrigation source and the traffic source were identified as the secondary control sources, with As, Pb, and Hg identified as the secondary control PTEs. This study revealed the status, risks, and sources of PTEs in black soils and provided a scientific basis of PTEs control for policymakers.

Understanding the interrelationships between land use, climate change, and regional water yield is critical for effective water resource management and ecosystem protection. However, comprehensive insights into how water yield evolves under different land use scenarios and climate change remain elusive. This study employs the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models, Patch-generating Land Use Simulation (PLUS) model, and Geodetector within a unified framework to evaluate the dynamics of land use, water yield, and their relationships with various factors (meteorological, social, economic, etc.). To forecast the Land Use/Cover Change (LUCC) pattern of the Yellow River Basin by 2030, three scenarios were considered: economic development priority (Scenario1), ecological development priority(Scenario2), and cropland development priority(Scenario3). Climate change scenarios were constructed using CMIP6 data, representing low stress (SSP119), medium stress (SSP245), and high stress (SSP585) conditions. The results show the following: (1) From 2000 to 2020, cropland was predominant in the Yellow River Basin, Henan Province, with significant land conversion to impervious land (construction land) and forest land; (2) Water yield changes during this period were primarily influenced by meteorological factors, with land use changes having negligible impact; (3) By 2030, the water yield of Scenario1 is highest among different land use scenarios, marginally surpassing ecological development priority by 0.5%; (4) Climate scenarios reveal significant disparities, with SSP126 yielding 67% higher water production than SSP245, driven predominantly by precipitation; (5) Geodetector analysis identifies precipitation as the most influential single factor, with significant interactions among meteorological and socio-economic factors. These findings offer valuable insights for policymakers and researchers in formulating land use and water resource management strategies.

Open-pit uranium mining in the south Texas Gulf Coast region was active during the second half of the 20th century. Although some mines have extensive records documenting the locations and extents with descriptions of reclamation procedures and timelines, abandoned and reclaimed mines with minimal documentation concurrently exist where little is known about locations, mine waste volumes, and reclamation status. Advanced mapping of these mining operations is helpful for producing basic mine land inventories and assessments of mine waste; identifying potential undeveloped resources and environmental health issues; and for developing proper management, restoration, and reclamation strategies. We studied known reclaimed and unreclaimed mine sites to develop a spatiotemporal mapping approach utilizing multiple techniques in remote sensing as a proof of concept. A mapping classification system was developed to identify uranium mine waste-rock piles, mine pits (with and without waste water), the total area of land disturbance due to mining, and to differentiate abandoned and reclaimed mine features using lidar (Light Detection and Ranging) analysis and multispectral temporal analysis. Visual interpretation of Landsat imagery and time-series analysis revealed the temporal life-cycle of mining, historical areas of disturbance, and reclamation; however, mine features below vegetation canopies are not always visible in satellite imagery. Lidar laser scanning penetrates the vegetation canopy to measure the topographic elevation and exposes anthropogenic modifications to the landscape. Lidar analysis (bare earth elevation surface, slope, topographic contours, topographic textures, and overland-flow hydrography) revealed mine features, and expansive areas of mining disturbance. The spatiotemporal mapping approach proved to be useful for identifying and characterizing known mine features, areas of disturbance, and reclamation status. Historical pre-mining topographic data were available at only 4 of the mines for comparison where the cut-and-fill tool estimated 21% less waste-rock volume, indicating that information about pre-mining terrain topography and/or subsurface mine waste geometry is needed to improve accuracies for quantifying buried mine waste volume at reclaimed mine sites.

Extracting the economic value by integrated utilization of space in old communities is crucial for encouraging independent participation from enterprises and residents, reducing reliance on government leadership and fiscal investment. This study starts from the active perspective of spatial assets, constructs a value activation framework for old community spatial assets by balancing "endogenous demand" and "exogenous opportunities." By enhancing "economic value" through "use value," five methods for value extraction and overall project utilization paths are proposed, guided by a dynamic "cost-revenue" balance. Using multi-source data, we identify the spatial assets of 1,096 old communities in central Guangzhou and apply a market comparison method for economic value assessment. Additionally, the study offers recommendations on timing and project portfolios for regeneration efforts, along with strategies for establishing a coordinating implementation entity and fund account. This research provides strategic insights for advancing the regeneration of old communities by tapping into their macro-level economic potential.

A 7.4 m long sediment core has been retrieved from the central part of Lake Vrana on the Island of Cres to reconstruct the paleoenvironmental conditions. Lake Vrana is the deepest freshwater lake in Croatia, located in the karst region of the eastern Adriatic coast. A dated sediment sequence in Lake Vrana of 4.4 m spans the past 16.4 kyr, and features a dynamic sediment deposition until the beginning of the Holocene, including strong sediment input and supply to the lake by runoff sediments of dolomitic origin from the catchment in the period 16.4-14.4 cal kyr BP. High organic carbon content, which originates from mixed terrestrial and aquatic origins in the periods 14.4-13.3 cal kyr BP and 12.7-11.7 cal kyr BP, indicates fluctuating lake levels in shallow water environments during the late glacial to Holocene transition. The Holocene sequence indicates the development of more stable conditions and continuous sediment deposition, characterized by increasing trend of siliciclastic sediments delivered into the lake during the early Holocene (11.7-10 cal kyr BP), and dominantly from 8 to 4.4 cal kyr BP, indicating enhanced input and erosion, which coincides with humid and pluvial period, recorded in the central Mediterranean region. It is followed by sediments with high organic carbon content between 4.4 to 1.6 cal kyr BP, which points to higher lake productivity. Calcite sedimentation prevail between 1.6 to 0.4 cal kyr BP, indicating stable deeper-lake conditions, while dominantly siliciclastic sediments in the period 0.4-0.1 cal kyr BP indicate erosion during Little Ice Age (LIA), with enhanced precipitation and sediment discharge from the catchment. The re-establishment of calcite sedimentation has been observed over the last 100 years.

Compared to the southern Tibetan Plateau, the northern part has been regarded as relatively lacking geothermal resources. However, there is no lack of natural hot springs exposed in beads along large-scale fracture systems, and research on them is currently limited to individual hot springs or geothermal systems. This paper focuses on the Wahongshan-Wenquan Fracture Zone (WWFZ), analyzes the formation of five hydrothermal activity zones along the fracture zone in terms of differences in the hydrochemical and isotopic composition of the hot water, and then explores the hydrothermal control of hot springs in the zone by the fracture. The results show that the main Fractures of WWFZ is the regional heat-control structures, and the fracture system formed by a small number of near-north-south and near-east-west fractures transversal to it provides a favorable channel for deep hydrothermal convective circulation. Ice and snow melt water from the Ngola Shan Mountains, with an average elevation of more than 4,500 meters above sea level, infiltrates along the fractures and is heated by deep circulation to form deep geothermal reservoirs. There is no detectable contribution of mantle source heat to the hot spring gases, and the heat source is mainly natural heat conduction warming, but the "Low-Velocity body (LVB)" in the middle and lower crust of the area may be the primary heat source of the high geothermal background in the area. The hydrochemical components of the hot springs in the area show a certain regularity, and the main ionic components, TDS and water temperature, tend to increase away from the main rupture, reflecting the controlling effect of the WWF on the hydrothermal transport in the area. In the future, the geothermal work in this area should focus on the hydrothermal control properties of different levels, the nature of fractures in the area, and the thermal contribution of LVB in the middle and lower crust.

In this study, the separation conditions of UHPLC-QTOF-MS and the extraction conditions of QuEChERS were optimized. The analytical process for determining Broflanilide residues in different soil types was successfully established and applied to its adsorption, desorption and leaching in soil. Broflanilide was extracted from soil with acetonitrile as extraction solvent, and purified with 50 mg PSA + 150 mg MgSO4. The modified UHPLC-QTOF-MS method was used for determination. In different types of soil, the average recovery of Broflanilide was 87.7% -94.38%, and the RSD was lower than 7.6%. In the analysis and determination of the adsorption, desorption and leaching properties in four soils, the RSD was less than 9.2%, showing good stability and can be applied to the analysis and determination of the residue of Broflanilide in different soils.

Successful geophysical exploration works in Gucheng-Yaxi area located in Gaochun District, Jiangsu province, China, have been limited partly due to complex geological condition of the area and high artificial noise in data acquired using electrical and electromagnetic methods. In this work, we deployed the new anti-interference spread spectrum induced polarization method (SSIP) and audio magnetotellurics (AMT) method to detect a copper-polymetallic deposit in the area. Two-dimensional (2D) inversion results at Gucheng-Yaxi Area revealed a high chargeability anomalous zone on SSIP profile that coincided with a zone of moderate resistivity located between two resistor bodies on AMT profile. A follow up 1200 meters drill hole was established at this high chargeability-moderate resistivity zone which encountered polymetallic (copper, lead, zinc, gold and silver) mineralization at the depth ≥ 400 m. Drill hole data analysis showed that mineralization occurs interspaced in marble rock mass at varies depth. Furthermore, several low resistivity-weak chargeability sections were revealed and inferred attributed to Cretaceous sediments and faults. These faults are thought to have played critical role in the polymetallic mineralization genesis. In summary, this work demonstrated the successful of application of SSIP and AMT at detecting a metallic deposit in an area with high artificial noise. Hence the geophysical prospection potential of Gucheng-Yaxi area is great.

This article aims to assess Pakistan's National Spatial Data Infrastructure implementation efforts through well-established approaches including the SDI readiness model, organizational aspects, and state of play. The findings underscored challenges related to human resources, SDI education/culture, long-term vision, GI awareness, sustainable funding, metadata availability, online geospatial services, and geospatial standards hindering the NSDI development in Pakistan. However, certain factors, such as the legal framework for NSDI establishment, web connectivity, geospatial software availability, core spatial dataset availability, and institutional leadership, exhibit favorable standings. Thus, to enhance the development of NSDI for Pakistan, recommendations include bolstering financial and human resources, improving online geospatial presence, and fostering a long-term vision for NSDI.

The Himalaya is well known for high diversity and ethnobotanical uses of medicinal plants. However, not all areas of the Himalayas are well studied. In particular, studies on ethnobotanical uses of plants from the Eastern Himalayas are rare and lacking for many tribes. Past studies primarily focused on listing plants name and their traditional medicinal uses. However, studies on traditional ethnopharmacological practices on medicine preparation had not yet been reported in published literature from the Eastern Himalaya. In this study, we are reporting the first time ethnopharmacological used 24 medicines, their procedures of preparation and listed 53 plant species used for those medicines for Monpa tribe. Such documentations had not yet been done for other tribes in India. Our research demonstrates the urgent need to documents traditional medicine preparation procedures from the local healers before rapid cultural modernization forgets them in transforming country like India. This study should motivate national and international researchers to do more works on ethnopharmacology and bioprospecting.

The mineralogical indices, e.g., Quartz Index (QI), Carbonate Index (CI), Mafic Index (MI) for ASTER multispectral thermal infrared (TIR) data have been applied to various geological materials. The indices are proved to be robust in extracting geological information at the local scale. Considering the relatively narrow spatial coverage of each ASTER scene compared to LANDSAT, many scenes are needed when mosaicking the images to be mapped at regional scales. We develop a system to search ASTER data for a target area given the vast and expanding ASTER data archive. The data is then conditioned, prioritized, and the indices are calculated before finally mosaicking the imagery. The maps of the indices covering the very wide region of the central Tibetan Plateau are produced with this system. The characteristic features of the indices relating to their geology in the study area are analyzed and discussed. Many interesting lithological and structural information that are not currently well understood in the central Tibetan Plateau, the highest and most extensive plateau in the world, with an average elevation of over 4,500 meters above sea level, for example, distributions of the mafic-ultramafic rocks along the suture zones, the quarzitic and marine sedimentology accreted to the Eurasian continent and sulfate layers related to the Tethys and neo-Tethys geological setting can be retrieved from the processed ASTER images.

Three-dimensional (3D) geological modeling technology offers intricate geological information services and technical support for the research of urban geological conditions and their evolutionary mechanisms. It further facilitates the analysis of environmental geological problems, encompassing their distinguishing features and genetic mechanisms. The precision of such three-dimensional models is predominantly influenced by the caliber of modeling data, the chosen modeling methodologies, and the intricacy of the modeled geological objects. This research, exemplified by the three-dimensional geological structure modeling of the Wuhan metropolitan development area, aims to explore a comprehensive three-dimensional modeling framework for urban geology, particularly in scenarios with large-scale and complex geological conditions. This study holds significant technical value in enhancing the application of 3D geological modeling, as well as practical importance for urban geological investigations and research.

Carpels are a reproductive feature restricted to angiosperms, therefore they are a focus of many botanical studies. However, there are controversies over the nature of carpels. A reason underly-ing these controversies is mixing implications given by conflicting interpretations on fossil carpels in early fossil angiosperms from the Cretaceous. These controversies hinder a clear understanding of angiosperm evolution and systematics. A key to these questions is older fossil fruits bearing concerned information. Here we report a new fossil fruit, Xenofructus dabuensis gen. et sp. nov, from the Middle Jurassic of Liaoning, China. Unlike previously reported fruits of early angio-sperms that were interpreted as bearing seeds either on adaxial or abaxial margin by various au-thors, our fossil demonstrates clearly that the seeds in Xenofructus are neither borne on the adaxial nor abaxial margin of the fruit, instead the seeds of Xenofructus are borne on an axis positioned between two margins. This new feature implies that a placenta in carpels is an ovule/seed-bearing axis, a carpel is a composite organ comprising an enclosing leaf (fruit wall) and an axis (placenta). The adaxial or abaxial position of ovules/seeds frequently seen in fossil and extant angiosperms is a consequence derived through long time evolution (coalescence of placenta with either margin of fruits). Carpels can be taken as foliar structures enclosing their associated ovulate branches.

As matrix representations of magic labelings of related hypergraphs, magic squares and their various variants have been applied to many domains. Among various subclasses, trimagic squares have been investigated for over a hundred years. The existence problem for trimagic squares with singly even orders and orders 16n has been solved completely. However, very little is known for the existence of trimagic squares with other even orders except for the only three examples and three families. We construct normal trimagic squares by using product constructions, row-square magic rectangles, and trimagic pairs of orthogonal diagonal Latin squares. We give a new product construction: For positive integers p, q, and r having the same parity other than 1, 2, 3, 6, if normal p × q and r × q row-square magic rectangles exist, then a normal trimagic square with order pqr exists. As its application, we construct normal trimagic squares of orders 8q^3 and 8pqr for all odd integers q not less than 7 and p,r ∈ {7, 11, 13, 17, 19, 23, 29, 31, 37}. Our construction can easily be extended to construct multimagic squares.

This research investigates precision enhancement in regional quasi-geoid refinement through ADS80 tri-linear array scanning stereoscopic imagery for aerial triangulation, coupled with GNSS gravity-potential modeling. Acquiring stereoscopic imagery and analyzing triangulation accuracy with the ADS80 camera, we performed this study over the Qinghai-Tibet Plateau's elevated, desolate terrain, collecting 593 GNSS points following high-precision stereoscopic imagery model. Utilizing 12 gravity satellite models, we computed geoid heights and China 1985 Yellow Sea elevations for 28 benchmarks and GNSS points, thereby refining the Qinghai Province Quasi-Geoid Model (QPQM) using geometric techniques. The findings reveal that POS-assisted ADS80 stereoscopic imagery yields high-precision triangulation with maximal horizontal and elevation accuracies of 0.083/0.116 cm and 0.053/0.09 cm, respectively, across five control point arrangements. The RMSE of normal heights for 1985, processed via these GNSS points, achieved decimeter precision. By applying error corrections from benchmarks to the 1985 elevation data from gravity satellites and performing weighted averaging, the precision of EGM2008, SGG-UGM-2, and SGG-UGM-1 models improved to 8.61 cm, 9.09 cm, and 9.38 cm, respectively, surpassing the QPQM by 9.22 cm to 9.99 cm. This research demonstrates that the proposed methods can significantly enhance the precision of regional quasi-geoid surfaces. Additionally, these methods offer a novel approach for rapidly establishing regional quasi-geoid models in the uninhabited areas of the Qinghai-Tibet Plateau.

The Double U-pipe ground heat exchanger, recognized for its straightforward process, cost-effectiveness, high heat exchange efficiency, and low thermal resistance, stands as the predominant ground heat exchanger type in today's shallow geothermal energy development and utilization. Recent years have seen significant research into the heat transfer influencing factors and heat exchange performance of Double U-pipe ground heat ex-changers via experimental testing methods. Yet, studies integrating numerical simulation with in-situ testing have been less frequent. Drawing on the cylindrical heat source model theory and the outcomes of regional in-situ thermal response tests, this paper develops a Double U-pipe ground heat transfer model by establishing physical, mathematical, and heat transfer geometric models. It assesses the impact of varying inlet temperatures, flow rates, and initial ground temperatures on heat exchange efficiency under heating conditions. The results validate the precision of the double U-pipe ground heat exchanger model based on in-situ testing. They show that enhancing the temperature differential between the inlet and initial temperatures, elevating the initial ground temperature, and moderately increasing the flow rate can boost the system's heat exchange efficiency.

This study uses the Scopus and Web of Science databases to review the forecast of electricity consumption from 2015 to 2024. Based on the keywords used in the article title, which are associated with electricity consumption forecasts, the study retrieved 821 docu-ments for additional analysis using various techniques. For frequency analysis, we used Mi-crosoft Excel; for data visualisation, we used VOSviewer; and for citation metrics and analysis, we used Harzing's Publish or Perish. Standard bibliometric variables, including publication growth, authorship patterns, collaboration, prolific authors, national contribution, most active institutions, favourite journals, and highly cited articles, are used in this study's reporting of the findings. Our analysis indicates that over ten years, starting in 2015, publications on the forecasting of electricity consumption have continued to increase. China was the country that contributed the most to research on electricity consumption. Most of the research publications on electricity consumption were published in the Journal of Energy.

The Sanhetun tellurium-gold (Te-Au) deposit, located in the Duobaoshan polymetallic metallogenic belt (DPMB) within the eastern section of the Central Asian Orogenic Belt (CAOB), is a newly discovered small scale gold deposit. The mineralization, with resource of ≥ 4t Au, is mainly hosted in 3 NNE-trending alteration zones between granitic mylonite and volcanogenic-sedimentary formations. It’s the genesis and timing of formation of this deposit are poorly constrained. Here, we report the results of petrographic studies, TESCAN Integrated Mineral Analyzer (TIMA), major and trace element concentrations, and in situ S isotope of pyrite and U-Pb ages of calcite. The results show that there are five species of telluride, including native tellurium, tetradymite, tsumoite, hessite and petzite and four types of pyrite, including coarse-grained euhedral Py1, fine-grained quartz-Py2 vein crosscutting Py1 and anhedral aggregated Py3 and Py4. The pre-ore Py1, containing negligible Au, Te and other trace elements, have a relatively narrow range of δ34S values ranging from -1.20 to -0.57‰. The Py2 have slightly high concentrations of Au and Te and distinctly high concentrations of Mo, Sb, Zn and Mn with markedly positive δ34S values of 4.67 to 14.43‰. The main ore stage Py3 contains high Au and Te concentrations and shows narrow δ34S values ranging from -5.69 to 0.19‰. The post-ore stage Py4 contains low Au concentrations with the δ34S values ranging from 2.66 to 3.86‰. Calcite from ore stage yield a U-Pb age of 135 ± 22 Ma, interpreted as the age of Te-Au mineralization. Our geochemical and geochronological data indicate that the Te was derived from partial melting of mantle metasomatized by the Te-rich fluids that originated from oceanic sediments. We correlate the tectonic setting with the subduction of Paleo Pacific plate in Early Cretaceous.

The emerging working mode of coordinated trucks and drones has demonstrated significant practical potential in various fields, including logistics and delivery, intelligence surveillance reconnaissance, area monitoring, and patrol. The seamless collaboration between trucks and drones is garnering widespread attention in academia and has emerged as a key technology for achieving efficient and secure transportation. This paper provides a comprehensive and in-depth review of the research status on the routing problem for coordinated trucks and drones, covering aspects such as application background, cooperative modes, configurations, issues have been taken into consideration, and solution methodologies.

The Huichang fault Basin in Jiangxi Province exhibits high terrestrial heat flow values and a dense concentration of hot springs. However, existing research is limited to individual geothermal fields without a comprehensive basin-wide analysis of geothermal water origins. This study investigates the source of geothermal water across five representative geothermal fields using hydrochemical components and isotopic analyses. The findings indicate: (1) The hydrochemical type transitions from bicarbonate sulphate-sodium-calcium in the north to bicarbonate sodium-calcium in the south; (2) Major chemical components of the geothermal water derive from evaporative salt rocks (Cretaceous sand conglomerate cover) and siliceous formations (granite/metamorphic rock basement); (3) The recharge elevation for geothermal water is approximately 1 km, sourced from adjacent hills. The central basin's geothermal water has an apparent age of about 26,000 years, with δ¹³C indicating participation in deep circulation; (4) Thermal reservoir temperatures range from 74 to 113℃, with the lowest temperatures at the basin's edges (Anzishan and Shangjin geothermal fields) and higher temperatures towards the center, suggesting increased warming and a circulation depth of 1.5 to 2.5 km; (5) Simulation results of reverse water-rock interaction indicate that mineral precipitation-dissolution processes are governed by the basin's main thermal control structures. The geothermal fields are predominantly influenced by reduction alterations associated with granite and are closely linked to paleo-geothermal waters in the enclosed basin, with the formation of these fields heavily reliant on the heat production or conduction from radioactive elements in granite.

This paper incorporates the definition of Intrusion Detection Systems and the methodologies utilised by these systems. As well as this, this research paper also encompasses a taxonomy and a survey of IDS and the specific strategies and principles. Finally, this paper also includes a discussion amongst other authors for instance what the authors differ and agree on, along with the previously related studies.

The increasing request of digitized data among several fields, including the built environment and Cultural Heritage (CH) one points out the need of proficient ways to access, archive and share 3D data and related information among users. The sector of reality capture produces accurate and reliable products that can support building management and CH maintenance, at the price of heavy and resource-demanding data. An emerging solution to this problem is represented by the web platforms for 3D data management, that promise to relieve users from the costs of archive and hardware, providing effective visualization, access and sharing tools. The panorama of commercial web platforms is analyzed according to the Software as a Service business model and the features of some representative platforms are exposed. The paper discusses the main advantages in terms of diffused access and collaboration and the potential issues concerning the long-term archive and data persistence. The future perspectives of the web platform sector are promising as, according to the current development path, could be able to empower the built environment and CH sector with a diffused, systematic and aware use of 3D data.

Within environmental perception, automatic navigation and object detection, computer vision is a crucial and demanding field with many applications in modern industries, such as multi-target long-term visual tracking in automated production, defect detection, and driverless robotic vehicles with sensing ability. The performance of computer vision has greatly improved recently thanks to developments in deep learning algorithms and hardware computing capabilities, which has spawned the creation of a large number of related applications. This paper presents the results of a detailed review of over 50 papers published over the course of two decades (1999–2024), with a primary focus on the technical advancement of computer vision. To elucidate the foundational principles, an examination of typical visual algorithms based on traditional correlation filtering was initially conducted. Subsequently, a comprehensive overview of the most recent advancements in deep learning-based computer vision techniques was compiled. Furthermore, a comparative analysis between conventional and novel algorithms was undertaken to discuss the future trends and directions of computer vision. Lastly, the feasibility of employing visual SLAM (Simultaneous Localization and Mapping) algorithms in the context of autonomous vehicles with sensing capability was explored. Furthermore, we explored a thorough and efficient architecture for utilizing these techniques in autonomous robotic vehicles into unmanned green and low-carbon smart factories, underlining novel advances as well as potential prospects for future research.

The rapid growth of the global population is causing a significant increase in waste production, leading to serious environmental and public health challenges. To address these issues, waste management systems are incorporating advanced technologies. Machine learning and computer vision are used to predict waste patterns, optimize collection schedules, and improve sorting accuracy. Deep learning automates the sorting process, provides predictive analytics, and enhances recycling rates. Robotics, combined with AI and computer vision, improves sorting efficiency, while the Internet of Things (IoT) monitors waste levels and optimizes collection routes. Despite these benefits, challenges such as data scarcity, high computational demands, and the need for substantial infrastructure investments must be addressed. This research explores the integration of advanced technologies into waste management and evaluates their effectiveness using waste datasets. It highlights the potential to tackle environmental challenges and lay the groundwork for more intelligent waste management solutions.

In our recent publications, based on strong and electroweak interactions, we have developed a completely new formula for estimating nuclear binding energy. With reference to currently believed Semi Empirical Mass Formula (SEMF), we call our formula as ‘Strong and Electroweak Mass Formula’ (SEWMF). Our formula constitutes 4 simple terms and only one energy coefficient of magnitude 10.1 MeV. First term is a volume term, second term seems to be a representation of free nucleons associated with electroweak interaction, third term is a radial term and fourth one is an asymmetry term about the mean stable mass number. In this paper, we make an attempt to understand and estimate the maximum binding energy associated with any mass number. It can be expressed as, for A > 4, $\left(BE\right)_A\cong \left[A-0.000935A^2-A^{1/3}-A^{-1/2}\right]$ MeV. We are working on refining the 4^th term with even-odd corrections, shell corrections and other microscopic corrections. Proceeding further, stable mass numbers and super heavy mass numbers can be understood with a relation of the form, $\left[\textrm{RoundOff}\left(\left(Z+2.9464\right)^{1.2}-1.7165\right)\right]\pm\left[0,1\right]\pm\left(2n\right)$ where $n \cong 0,1,2$. It needs a review with respect to even-odd proton numbers and other microscopic corrections.Very interesting point to be noted is that based on the concept of “binding energy per nucleon”, the most complicated Avogadro number and Unified atomic mass unit can be estimated in a unified approach.

Although the field of geomatics has seen multiple technological advances in the recent years which enabled new applications and simplified the consolidated ones, some tasks remain challenging, inefficient, and time- and cost-consuming. This is the case of accurate tridimensional surveys of narrow spaces. Static laser scanning is an accurate and reliable approach but impractical for extensive tunnel environments; portable laser scanning is on the other hand time-effective and efficient but not very reliable without ground control constraints. This paper describes the development process of a novel image-based multi-camera system meant to solve this specific problem: delivering accurate, reliable, and efficient results. The development is illustrated from the system conceptualization and initial investigations to the design choices and requirements for accuracy. The resulted working prototype has been put to the test to verify effectiveness of the proposed approach.

Nowadays, inquiries are one of the most important tasks to be executed in order to get valued information. When an inquiry is done to gather information about a human behavior there are some problems inherent to it. One of the main problems is how the information about many different persons can be processed to give good information about their environment. Most of the information gathered by these inquiries is used to analyze the behavior of the respondents. It can be used to estimate the success of a certain product on the market or just to validate the satisfaction of current costumers. The veracity of this information can be critical to model environments with some acceptable level of precision. Modelling environments through Artificial Neural Networks (ANN) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in deal with sets of data with some noise, but they are fundamentally surjective mathematical functions and they aren’t able to give different results for the same input. So if an ANN is trained using data where the same input has different images which can be the case of survey data, this can be a problem for the success of modelling the environment. Therefore is necessary to process the data in order to adjust and eliminate invalid data.

Graph Neural Networks (GNNs) are powerful models capable of managing intricate connections in non-Euclidean data, such as social networks, physical systems, chemical structures, and communication networks. Despite their effectiveness, the large-scale and complex nature of graph data demands substantial computational resources and high performance during both training and inference stages, presenting significant challenges, particularly in the context of embedded systems. Recent studies on GNNs have investigated both software and hardware solutions to enhance computational efficiency. Earlier studies on Deep Neural Networks (DNNs) have indicated that methods like reconfigurable hardware and quantization are beneficial in addressing these issues. Unlike DNN research, studies on efficient computational methods for GNNs are less developed and require more exploration. This survey reviews the latest developments in quantization and FPGA-based acceleration for GNNs, showcasing the capabilities of reconfigurable systems (often FPGAs) to offer customized solutions in environments marked by significant sparsity and the necessity for dynamic load management. It also emphasizes the role of quantization in reducing both computational and memory demands through the use of fixed-point arithmetic and streamlined vector formats. This paper concentrates on low-power, resource-limited devices over general hardware accelerators and reviews research applicable to embedded systems. Additionally, it provides a detailed discussion of potential research gaps, foundational knowledge, obstacles, and prospective future directions.

With the rapid development of wind power generations, the inertial response of wind turbines (WTs) are widely concerned recently, which is important for grid frequency dynamic and stability. This paper recognizes and understands the inertial response of type-3 and type-4 WTs from the view of equivalent internal voltage, in analogy with typical synchronous generators (SGs). Due to the dynamic of the equivalent inertial voltage different from SGs, the electromechanical inertia of WTs is completely hidden. The rapid power control loop and synchronization control loop is the main reasons that the WT's inertial response is disenabled. On the basis of the equivalent internal voltage's dynamic, the existing inertia control method for WTs are reviewed and summarized as three approaches from the view of WT's control, i.e. optimizing the power control or synchronization control or both. At last, the main challenges and issues of these inertia controls are attempted to explain and address.

The purpose of this paper is to construct an evaluation system for AI painting software based on generative adversarial network technology, which optimizes the performance of the related software in terms of functionality, ease of use, system performance and safety. The results of the questionnaires are statistically analyzed. In addition, exploratory factor analysis was supported to extract the data of the study, which was ultimately used to calculate the weight and importance of each index through fuzzy hierarchical analysis method. The study constructed an evaluation system for AI painting software based on generative adversarial network technology, including 16 indicators of functionality, 16 indicators of ease of use, 7 indicators of system performance, and 8 indicators of safety respectively that their alpha coefficients were 0.882, 0.962, 0.932, 0.932, and 0.932, respectively. In addition, the accumulated explanatory variances of their coefficients were 84.405%, 84.897%, 84.013%, and 72.606%, respectively, 73.013%, and 72.606%, respectively. It is clear that the items included in each of the indicators are homogeneous, with a high degree of internal consistency. This paper suggests that the development of AI painting software focusing on functionality, ease of use, system performance and safety can enhance the market competitiveness of the software.

In recent years, the scientific and technological developments led to an explosion of available videos on the web, increasing the necessity of fast and effective video analysis and summarization. Video summarization methods aim to generate a synopsis by selecting the most informative parts of the video content. The user’s personal preferences, often involved in the expected results, should be taken into account in the video summaries. In this paper, we provide the first comprehensive survey on personalized video summarization relevant to the techniques and datasets used. In this context, we classify and review personalized video summary techniques based on the type of personalized summary, on criteria, on the video domain, on the source of information, on the time of summarization, and on the machine learning technique. Depending on the type of methodology used by the personalized video summarization techniques for the summary production process, we classify the techniques into five major categories, which are feature-based video summarization, key frame selection, shot selection-based approach, video summarization using trajectory analysis, and personalized video summarization using clustering. We also compare personalized video summarization methods and present 37 datasets used to evaluate personalized video summarization methods. Finally, we analyze opportunities and challenges in the field and suggest innovative research lines.

Strip steel surface defect recognition research has important research significance in industrial production. Aiming at the problems of defect feature extraction difficulty, slow detection speed and insufficient dataset, YOLOv5 is improved on the basis of YOLOv5, and YOLO-LFPD (Lightweight fine particle detection) model is proposed. By introducing the RepVGG (Re-param VGG) module, the robustness of the model to the surface defect features of strip steel is enhanced, and the expressive ability of the model is improved. FasterNet is used to replace the backbone network, which ensures the accuracy and accelerates the inference speed, making the model more suitable for real-time monitoring. The use of pruning, GA genetic algorithm with OTA loss function method further reduces the model size while better learning the strip steel defect feature information, thus improving the generalisation ability and accuracy of the model. The experimental results show that the introduction of the RepVGG module and the use of FasterNet can well improve the model performance, with a reduction of 48% in the number of parameters, a reduction of 13% in the number of GFLOPs, an inference time of 77% of the original, and an optimal accuracy compared with the network models in recent years. The experimental results on the NEU-DET dataset show that the accuracy of YOLO-LFPD is improved by 3% to 81.2%, which is better than other models, and provides new ideas and references for the lightweight strip steel surface defect detection scenarios and application deployment.

The Qiushuwan Cu-Mo deposit, located in the East Qinling molybdenum belt, which is a major mining site recognized for its enormous reserves of medium-sized molybdenum. In order to enhance our comprehension and ascertain supplementary mineral resources, it is critical to conduct an in-depth investigation of both the deep and peripheral regions. The primary goal of this study is to investigate the Qiushuwan Copper Molybdenum Mine, which is located in Zhenping County, Henan Province. The spread spectrum-induced polarization (SSIP) method is utilized as a means of conducting mining exploratory analysis. Significant findings have been obtained through the collection of SSIP data, geological analysis, and electrical parameter study of rock and ore samples along profile-80. By conducting a meticulous examination of the SSIP data at suitable frequencies, two prominent conductive ore bodies, specifically C2 and C4, have been successfully discovered. C2 exhibits a correlation with granite porphyry and molybdenum veins, while C4 is linked to a skarn deposit that contains a concentration of copper ore. The spatial differences in conductivity within the research area have been precisely characterized using resistivity models obtained from the SSIP data. The precision and dependability of our conclusions have been confirmed through the integration of borehole data and the matching of geological sections with inversion models. The recovered core samples show a diverse range of minerals, containing both valuable ores and minerals that have undergone alteration. Pyrite constantly appears with molybdenite in the majority of samples. The aforementioned results provide a strong foundation for future exploration initiatives and sustainable resource exploitation, enabling informed decision-making in the effective use and administration of natural resources. This study adds to the existing body of knowledge on the Qiushuwan Cu-Mo deposit and improves exploration methods in the region by utilizing the SSIP approach. The findings are a useful asset for geologists, mining experts, and stakeholders, offering essential knowledge for future exploration and sustainable utilization of resources.

Based on three assumptions of our 4G model of final unification, we have developed simple logics for estimating the unified atomic mass unit, atomic electrochemical equivalents, Faraday constant and Avogadro number. We appeal the science community to review the following points. 1) Unified atomic mass or energy unit can be considered as ‘average rest energy of nucleons minus average binding energy per nucleon of all atomic nuclides plus electron rest energy’. 2) Electrochemical equivalent of any atomic ion is nothing but the ratio of its mass to its charge. 3) Faraday charge is nothing but the ratio of elementary charge to unified atomic mass unit. 4) Faraday mass can be considered as the inverse of Faraday charge. 5) Avogadro number is nothing but the inverse of unified atomic mass unit. 6) Number of atoms per kg can be called as Kg-mole and number of atoms per gram can be called as gram-mole. Following our 4G model, starting form A=5 to 340, for isobars, ‘average binding energy per nucleon’ having maximum binding energy can be estimated with a simple relation of the form, BE/A=A^-1[1-(0.5+0.000935A²)-A^1/3]10.1MeV. It needs a review and fine tuning.

Cocoa (Theobroma Cacao, L.) represents an important market that gained relevance and became an esteemed commodity thanks to cocoa powder, chocolate, and other related products. This work has analyzed 59 cocoa powder samples from the European market. Three distinct subgroups were identified: organic or conventional, alkalized or not alkalized, and raw or roasted processing. The impact of the technological process was evaluated on the pH, color, and compositional traits, as content of biogenic amines and salsolinol. The phenolic fraction was also investigated trough both common and emerging methods. Results depict that the influence of the agronomical practices (organic/conventional) did not affect significatively (p<0.05) the composition of cocoa powders; similarly, the roasting process was not discriminative for the compounds traced. On the other hand, the alkalinization process greatly impacted on color, and pH, no matter of cocoa provenience, obtention, or other processes, also resulting reducing the phenolic fraction of treated samples. Principal components analysis confirmed that the alkali process acts on pH, color, and phenolic composition, but not on other bioactives molecules (BAs and salsolinol). All samples resulted safe while alkalized powders have a great reduction in beneficial biocompounds. A novel strategy could be to emphasize non-alkalized powders in the label, to meet the demand for more beneficial products.

In this paper, we present a full coverage path planning (CPP) algorithm for the marine surveys conducted in the convex polygon shaped search area. The survey is supposed to carry out by torpedo-type AUVs (autonomous underwater vehicles). Due to their nonholonomic mechanical characteristics, these vehicles have nonzero minimum turning radius. For any given polygon shaped search area, it can always be partitioned into one or more convex polygons. With this in mind, this paper proposes a novel search algorithm called CbSPSA (Calculation based Shortest Path Search Algorithm) for full coverage of any given convex polygon shaped search area. By aligning the search inter-tracks alongside the edge with the minimum height, we can guarantee the minimum number of the vehicle’s turns. In addition, the proposed method can guarantee the planned path is strictly located inside the polygon area without overlapped or crossed path lines, and also has the total path length as short as possible. Considering the vehicle’s nonzero minimum turning radius, we also propose a sort of smoothing algorithm to smooth the waypoint path searched by CbSPSA so as for the vehicle to exactly follow it. The smoothed path is also guaranteed to be strictly located inside the polygon. Numerical simulation analyses are also carried out to verify the effectiveness of the proposed schemes.

This paper presents GeoFlood, a new open-source software package for solving shallow water equations (SWE) on a quadtree hierarchy of mapped, logically Cartesian grids managed by the parallel, adaptive library ForestClaw (Calhoun and Burstedde, 2017). The GeoFlood model is validated using standard benchmark tests from Neelz and Pender (2013) and against George (2011) results obtained from the GeoClaw software (Clawpack Development Team, 2020) for the historical Malpasset dam failure problem. The benchmark test results are compared against GeoClaw and software package HEC-RAS (Hydraulic Engineering Center - River Analysis System, Army Corp of Engineers) results (Brunner, 2018). This comparison demonstrates the capability of GeoFlood to accurately and efficiently predict flood wave propagation on complex terrain. The results from comparisons with the Malpasset dam break show good agreement with the GeoClaw results and are consistent with the historical records of the event.

We utilized camera traps to assess animal diversity, relative abundance, and anthropogenic activities in the Jhalana Reserve Forest (JRF), located amidst Jaipur, with a population of 3.9 million people. Between November 2017 and November 2019, camera traps were strategically deployed in both the tourist zone and peripheral areas, capturing 16,328 photos. This study represents the first comprehensive baseline assessment of animal diversity in JRF, documenting a total of 39 species, including 18 bird species, 14 mammals, and six domestic species, alongside human activity. Among the 14 mammal species, seven were carnivores. Notably, we identified 25 individual Indian leopards (Panthera pardus fusca) during 2017-2018, comprising 8 males and 17 females, highlighting the leopard as the apex predator in JRF. Concurrently, domestic animals accompanied by humans were observed within JRF. However, rigorous conservation efforts and patrols by the Rajasthan Forest Department resulted in a notable decline in human intrusion, reducing from 28.0% in 2017-18 to 3.9% in 2018-2019, with domestic animal activity reaching zero in the latter period. Consequently, the relative abundance of wildlife species increased during the study period of 2018-2019, underscoring the positive impact of conservation strategies implemented by authorities. Our findings affirm that camera traps effectively establish baseline data, assess animal diversity, and evaluate relative abundance in reserve forests situated within human-dominated landscapes. This study provides critical insights to inform the development and implementation of conservation strategies in similar protected areas.

Purpose: This study aimed to identify the risk factors for acute pancreatitis (AP) and its impact on outcomes in Polish children treated for ALL. Methods: The study group included 2303 children who received intensive chemotherapy for ALL. The group was subdivided into patients with at least one episode of AP and those who did not develop AP following treatment for ALL. Results: The cumulative incidence of AP in the study group was 4.08%. Older age was an independent risk factor for AP development (OR 1.05; 95%CI: 1.006 – 1.098; p=0.03). The overall mortality related to AP was 2.13%. The probabilities of disease-free survival (p-DFS) and event-free survival (p-EFS) were 0.84 vs. 0.86, log-rank: p=0.65 and 0.75 vs. 0.80, log-rank: p=0.12, respectively in both subgroups. A total of 22 out of 94 patients (23.4%) with AP were re-exposed to asparaginase (ASP) during the subsequent phases of the treatment. Only one re-exposed to ASP patient (4.5%) developed a second episode of AP. There were no significant differences in p-DFS and p-EFS between patients re-exposed and not re-exposed to asparaginase (0.78 vs. 0.86, log-rank p=0.27 and 0.63 vs. 0.79, log-rank p=0.09 respectively). Conclusions: The incidence of AP in children with ALL is low and related to the patient’s age. AP development doesn’t seem to impact p-DFS and p-EFS in children with ALL. The recurrence of AP following re-exposure to asparaginase in patients with ALL and a history of AP is low (4.5%). Re-exposure to asparaginase after the first episode of AP doesn’t improve either p-DFS or p-EFS in children with ALL.

From 2008 to 2024, the Newborn Screening Translational Research Network (NBSTRN), part of the National Institute of Child Health and Human Development (NICHD) Hunter Kelly Newborn Screening Program, served as a robust infrastructure to facilitate groundbreaking research in newborn screening (NBS), rare disease, and genomics. Over fifteen years, NBSTRN developed into a significant international network, supporting innovative research on novel technologies to screen, diagnose, treat, manage, and understand the natural history of more than 280 rare diseases. A key component of these efforts has been the development of resources and expertise for the newborn screening community in ethical, legal, and social issues (ELSI). This paper presents findings from a survey conducted by the NBSTRN team with input from one of theNBSTRN's expert workgroups, the Bioethics and Legal Workgroup. With responses from 88 members of the NBS community including researchers and state NBS programs, the survey found that individuals rely on internal and collegial ELSI resources and highlighted a substantial demand for structured, accessible training and resources. The survey’s insights emphasize the importance of privacy and informed consent in NBS research, and suggested a need for improved collaborative tools and educational programs focused on ELSI for the NBS community. The survey results may inform future efforts in ELSI and NBS research including the development of expanded ELSI tools that leverage digital platforms and consultative services.

The decarbonization of the electricity grid as one among the actions to reduce fossil fuel emissions, and thus their impact on global warming in the future, will be achieved mainly via the integration and widespread diffusion of renewable power sources. This is going to be supported also by the shift from the paradigm production-transmission-distribution, where electricity production oversees large-size power plants, to renewable-based distributed/diffused production, where electricity is generated very close or even by the same (group of) user(s) (or prosumers in the latter case). The number of mid-/small-size installations based on renewable energy technologies will therefore increase substantially, and the related renewable generation will be dominant against that from large-size power plants. Unfortunately, this will reduce the reliability of the grid very likely, unless appropriate countermeasures are taken/implemented, hopefully at the same time the paradigm shift is being achieved. To this aim, it is important to identify the anomalies and main fault causes that might determine in renewable-based power systems. This paper surveys the current state-of-the-art on anomalies and faults affecting wind-PV-storage hybrid power systems, i.e., the main renewable technology ensemble that will establish the future grid, and highlights possible research directions that may help to fill the literature gaps.

Understanding the dynamics of teamwork and communication among healthcare professionals is crucial in the face of evolving healthcare challenges. This study assessed relational coordination among healthcare professionals in the South Tyrolean healthcare system in Italy, focusing on communication and teamwork dynamics in a cross-sectional survey. Using the validated Relational Coordination Survey (RCS) instrument and 525 completed online responses, the questionnaire aimed to understand the implications of different levels of relational coordination ratings by general practitioners, hospital physicians, nurses, and administrative personnel (response rate 26%). The demographics of the participants revealed a predominance of female professionals (64%), with an average age of 50 and 18 years of service. The resulting RCS scores varied significantly across professional groups, with nurses reporting the highest within-group scores, indicating moderate coordination, and administrators reporting the lowest scores, reflecting areas of weak coordination. Between-group relational coordination was generally perceived as weak across professional groups, with the least weakness observed between general practitioners and nurses. German or Italian language and health district affiliation emerged as significant factors influencing relational coordination ratings, highlighting the need for differentiated understanding and strategies in multilingual and diverse regional settings. Assessments of interdisciplinary feedback and referral practices highlight the variation in teamwork and communication weaknesses and underscore the need for targeted interventions to improve relational coordination. This study provides insights into the complexity of relational dynamics in health care settings. It suggests that improving relational coordination through tailored strategies could significantly improve team effectiveness, quality of patient care, and overall system efficiency.

Real-Time Kinematic (RTK) is currently the most widely used method for positioning using Global Navigation Satellite Systems (GNSS) due to its accuracy, efficiency and ease of use. In forestry, position is a critical factor for numerous applications, with GNSS nowadays the preferred solution for obtaining such data. However, the decreased performance of GNSS observations in challenging environments, such as under the forest canopy, must be considered. This paper analyzes the performance of a survey-grade GNSS receiver under coniferous/deciduous tree cover, with short observation times of 30 seconds and corrections received in the field from a Continuously Operating Reference Station (CORS) of the national RTK network in Romania. 84 test points were determined, randomly distributed under the canopy, with reference data collected by topographical surveys using total station equipment. Relatively good accuracies were obtained, especially for horizontal position (RMSEs of 1.47 – 2.07 meters). The variation of GNSS performance under the different forest compositions was found to be statistically significant, while GNSS-specific factors such as DOP values only influenced the precision and not the accuracy of observations. We established that this methodology offers sufficient accuracy, application-dependent, even if the majority of GNSS solutions were code-based, rather than carrier phase-based, due to strong interference from the vegetation.

Background: In our recent publications pertaining to 4G model of final unification and based on strong and electroweak interactions, we have proposed the existence of a weak fermion of rest energy 585 GeV. Objective: To confirm the physical existence of the proposed 585 GeV weak fermion by analyzing weak and strong interactions in a unified approach via 4G model of final unification, super symmetry and string theory. Method: Considering the proposed nuclear charge of 2.95e, proton, electron mass ratio, specific charge ratios of proton and electron, Fermi’s weak coupling constant, Reduced Planck’s constant, nucleon magnetic moments, nuclear stability, nuclear binding energy, nuclear mass and neutron lifetime, it is planned to confirm the physical existence of the proposed 585 GeV weak fermion. Results: All proposed logics and formulae clearly establish the physical existence of 585 GeV weak fermion directly and indirectly. Conclusion: Believing in the physical existence of the proposed 585 GeV weak fermion, there is a scope for observing galactic TeV radiation coming by virtue of annihilation of 585 GeV fermions and radiation associated with various astrophysical acceleration mechanisms of 585 GeV fermions. Appeal: As we are beginners of astrophysics domain, we appeal the science community to see the possibility of considering the proposed 585 GeV weak fermion with a charge of $\pm\left(e\right)$ in place of electron and proton.

Despite the importance of influenza vaccination for healthcare workers (HCWs), their uptake remains challenging. This study aimed to measure influenza vaccine hesitancy among HCWs in Nakhon Phanom province, Thailand. A representative cross-sectional survey was conducted at six hospitals during August – September 2020 using a self-administered questionnaire. HCWs delaying getting influenza vaccines, accepting but unsure, or refusing with doubts were categorized as hesitant. Those accepting without any doubts were classified as non-hesitant. Vaccine hesitancy determinants were identified by multivariable logistic regression analysis. A total of 338 participants (97%) completed the questionnaires; most were female nurses working at district hospitals without pre-existing chronic medical conditions. Nearly 60% of participants (197/338) exhibited influenza vaccine hesitancy, and significant factors included age above 50 years adjusted odds ratio [aOR] 3.2, 95% CI 1.3-8.5), fair knowledge of influenza/vaccination (aOR 0.4, 95% CI 0.2-0.8), and negative influence of other HCW (High level– aOR 2.3, 95% CI 1.1-4.8; Moderate level- aOR 2.1, 95% CI 1.1-4.4). Imparting updated information to the HCW combined with positive guidance from influential peers may help reduce this hesitancy.

Groundwater represents a pivotal asset in conserving natural water reservoirs for potable consumption, irrigation, and diverse industrial uses. Nevertheless, human activities intertwined with industry and agriculture contribute significantly to groundwater contamination, highlighting the critical necessity to appraise water quality for safe drinking and effective irrigation. This research primarily focused on employing the Water Quality Index (WQI) to gauge water's appropriateness for these purposes. However, the generation of an accurate WQI can prove time-intensive owing to potential errors in sub-index calculations. In response to this challenge, an artificial intelligence (AI) forecasting model was devised, aiming to streamline the process while mitigating errors. The study collected 422 data samples from Mirpurkash, a city nestled in the province of Sindh, for a comprehensive exploration of the region's WQI attributes. Furthermore, the study probed into unraveling the interdependencies amidst variables in the physiochemical analysis of water. Diverse machine learning classifiers were employed for WQI prediction, with findings revealing that random forest and gradient boosting eclipsed other algorithms, achieving an accuracy rate of 99%. In close pursuit were SVM and XGBoost, registering accuracy scores of approximately 95% and 93%, respectively, while KNN and Decision Trees garnered accuracy rates of 88% and 87%, respectively. In addition to WQI prediction, the study conducted an uncertainty analysis of the models using the R-factor, providing insights into the reliability and consistency of predictions. This dual approach, combining accurate WQI prediction with uncertainty assessment, contributes to a more comprehensive understanding of water quality in Mirpurkash and enhances the reliability of decision-making processes related to groundwater utilization

This research addresses a gap in the literature by conducting a comprehensive analysis of patients’ level of satisfaction with dental care. Methods: By combining quantitative and qualitative survey methods with the PSQ questionnaire this study aims to augment ongoing initiatives to enhance dental patients’ experiences by painting a more comprehensive depiction of patients' level of satisfaction; Results: When asked about their overall level of satisfaction 77,1% of the patients said they received excellent services from the office personnel and 72,2% said they trust their doctors; Conclusions: Assessing patient satisfaction in the realm of dental services quality is crucial for enhancing service quality and accuracy, benefiting both patients and dentists, and ultimately improving public health.

End-to-end learned image compression codecs have notably emerged in recent years. These codecs have demonstrated superiority over conventional methods, showcasing remarkable flexibility and adaptability across diverse data domains while supporting new distortion losses. Despite challenges such as computational complexity, learned image compression methods inherently align with learning-based data processing and analytic pipelines due to their well-suited internal representations. The concept of Video Coding for Machines has garnered significant attention from both academic researchers and industry practitioners. This concept reflects the growing need to integrate data compression with computer vision applications. In light of these developments, we present a comprehensive survey and review of lossy image compression methods. Additionally, we provide a concise overview of two prominent international standards, MPEG Video Coding for Machines and JPEG AI. These standards are designed to bridge the gap between data compression and computer vision, catering to practical industry use cases.

Machine learning (ML) has transformed the financial industry by enabling advanced applications such as credit scoring, fraud detection, and market forecasting. At the core of this transformation is deep learning (DL), a subset of ML that is robust at processing and analyzing complex and large datasets. This paper provides a concise overview of key deep learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Deep Belief Networks (DBNs), Transformers, Generative Adversarial Networks (GANs), and Deep Reinforcement Learning (Deep RL). The study examines their learning processes, mathematical foundations, and practical applications in finance. It also explores recent advances and emerging trends in the financial industry alongside critical challenges such as data quality, model interpretability, and computational complexity, offering insights into future research directions that can guide the development of more robust and explainable financial models.

In our recent publications pertaining to 4G model of final unification and based on strong and electroweak interactions, we have developed a completely new formula for estimating nuclear binding energy. With reference to currently believed Semi Empirical Mass Formula (SEMF), we call our formula as ‘Strong and Electroweak Mass Formula’ (SEWMF). Our formula constitutes 4 simple terms and only one energy coefficient of magnitude 10.1 MeV. First term is a volume term, second term seems to be a representation of free nucleons associated with electroweak interaction, third term is a radial term and fourth one is an asymmetry term about the mean stable mass number. Considering this kind of approach, nuclear structure can be understood in terms of strong and weak interactions and cold nuclear fusion like complicated concepts can be understood in a theoretical approach positively. In this paper, by considering the accountability of we are making an attempt to improve the applicability and effectiveness of SEWMF. Number 0.00125 being a workable electroweak coefficient for Z=6 to 132 and A=2Z to 3.5Z, number of free nucleons assumed to be associated with electroweak interaction can be expressed as Improved asymmetry term can be expressed as, where represents the light house like stable mass number. With reference to old version, our improved binding energy formula can be expressed as, We are working on its possible modification for mass numbers less than the stable mass numbers. Following this approach, it is possible to show that nuclear binding energy scheme is associated with strong and weak interactions and is practically independent of Coulombic repulsions. Proceeding further, currently believed cold nuclear fusion assumed be associated with hydrated metals can be understood and can be confirmed with strong and weak interactions independent of coulombic repulsions. We are very confident to say that our approach will certainly motivate modern science community in this new direction.

The River Habitat Survey (RHS) is a method for assessing the conditions of a river channel and valley, taking into account hydromorphological factors, and assessing the degree of naturalness or the degree of anthropogenic alteration within the channel and valley based on physical and landscape data. The hypothesis tested in this study was that the quality of river habitats demonstrated using the RHS should translate into the composition of their fauna. We investigated aquatic bugs (Heteroptera Aquatica), which are a group with considerable dispersal abilities and, at the same time, composed of stenotopic species associated with a particular environment as well as of eurytopic elements inhabiting a wide range of environments (REF). The research was carried out in a small lowland river, the River Krąpiel (north-western Poland) at 6 sites with 43 sub-sites included. Our analyses suggest the RHS method can predict the composition and quality of the aquatic bug fauna inhabiting a given river. Indicators of river habitat quality (HQA and RHQ) will be associated with different habitat types depending on the geographic location and type of river, once indicating habitats associated with the mainstream river and mineral substrate (mountain and small lowland rivers) and at other times habitats covered by vegetation with relatively slow water flow (large and medium lowland rivers). Therefore, RHS may be a good method for estimating the water bug fauna, linking it to the size and character of the river.

Traditional computer vision techniques aim to extract meaningful information from images but often depend on manual feature engineering, making it difficult to handle complex real-world scenarios. Fractional Calculus (FC), which extends derivatives to non-integer orders, provides a flexible way to model systems with memory effects and long-term dependencies, making it a powerful tool for capturing fractional rates of variation. Recently, Neural Networks (NNs) have demonstrated remarkable capabilities in learning complex patterns directly from raw data, automating computer vision tasks and enhancing performance. Therefore, the use of fractional calculus in neural network-based computer vision is a powerful method to address existing challenges by effectively capturing complex spatial and temporal relationships in images and videos. This paper presents a survey of Fractional Calculus Neural Network-based (FC NN-based) computer vision techniques for denoising, enhancement, object detection, segmentation, restoration, and NN compression. The survey compiles existing FFC NN-based approaches, elucidates underlying concepts, and identifies open questions and research directions. By leveraging FC's properties, FC NN-based approaches offer a novel way to improve the robustness and efficiency of computer vision systems.

In this paper, we make an attempt to estimate the famous Avogadro number with programming logics written in Python associated with advanced nuclear binding energy formulae. Average rest mass of nucleon, nuclear binding energy per nucleon and electron rest mass seem to play a vital role in estimating the unified atomic mass unit and Avogadro number. Interesting point to be noted is that, Avogadro number seems to be the inverse of the Unified atomic mass unit. With further study, it seems possible to estimate the unified atomic mass unit and Avogadro number accurately. Our interesting observation is that, short range strong nuclear force seems to have a vital role in deciding the accuracy of Avogadro number. For that purpose one may consider AI techniques along with newly observed atomic nuclides and their nuclear binding energies. For Z=6 to 118 and A_low=2Z and A_upper=3.5Z, estimated average value of Avogadro number is $N_{Average} \cong 6.01938 \times 10 ^{26}$. Considering the saturation of nuclear binding energy, at Z=26, $N_{Z=26} \cong \left(6.02229 \textrm{to} 6.02285\right) \times 10 ^{26}$. At academic level, our proposal can be given a chance as a case study. It is planned to develop a web application for this purpose.

Packaged water consumption has grown rapidly in urban areas of many low and middle income countries, but particularly in Ghana. However, the sources of water used by this growing packaged water industry and its implications for water resource management and transport-related environmental impacts have not been described. This study aimed to assess the spatial distribution of regulated packaged water production in Ghana, both in relation to demand and for natural mineral water, to hydrogeological characteristics. 764 addresses for premises licenced to produce packaged water from 2009-2015 were mapped and compared to regional sachet water consumption and beverage import/export data examined. We find evidence to suggest packaged water is transported shorter distances in Ghana than in developed countries. For natural mineral waters, producers should be able to address the most widespread water quality hazards (including high salinity, iron and nitrates) in aquifers used for production through reverse osmosis treatment. The study suggests there is scope to integrate beverage product and groundwater regulatory databases to support groundwater management.

Metaverse represents a multifaceted digital universe integrating diverse technologies to create immersive, interconnected virtual environments. Key components foundational to its structure include the infrastructure layer for hardware and network support, the human interface layer utilizing virtual reality (VR) and augmented reality (AR) devices, and a decentralized layer leveraging blockchain for secure digital asset management. Further components encompass spatial computing for realistic 3D environments, the creator economy for content generation and monetization, and discovery mechanisms for navigation within Metaverse. VR, AR, blockchain, AI, the fifth-generation (5G) networks, and the Internet of Things (IoT) are central to Metaverse functionality, enabling real-time interactions and data integration. Real-Time Metaverse aims to replicate instantaneous real-world interactions, requiring advanced technological infrastructure for seamless data exchange. Critical challenges include ensuring low latency, high bandwidth, scalability, and robust security measures. Metaverse’s potential spans various domains, promising to revolutionize social interactions, entertainment, workplaces, and education through dynamic, immersive experiences. Ongoing advancements in high-performance computing, cloud services, and AI-driven technologies are essential for addressing existing barriers and achieving a cohesive, secure, and inclusive Metaverse ecosystem. Future research must focus on enhancing user experience, accessibility, and standardized interoperability to fully realize the transformative capabilities of a Metaverse.

During public health events, Active Mobility (walking and cycling) plays an important role in securing residents' travel. To enhance the resilience of urban transportation, this article integrates the travel environment elements for walking and cycling and constructs a travel environment for Active Mobility evaluation index system. This article uses survey data from the main urban area of Harbin in the context of the COVID-19 pandemic in May 2022, and combines the Gradient Boosting Decision Tree (GBDT) and Impact-Asymmetry Analysis (IAA) methods to analyze the satisfaction of the travel environment for Active Mobility. It proposes strategies for optimizing the travel environment for Active Mobility, ensuring residents' travel during public health events, guiding them to adopt Active Mobility, and improving urban transportation resilience. This research shows that Active Mobility can enhance urban transportation resilience. In addition, there are differences in resident demand for walking and cycling environments in the travel environment for Active Mobility. Residents are more concerned about the comfort of the walking environment, while they prioritize the convenience of the cycling environment.

Grid modernization has been ushered in by rising electricity consumption, deteriorating infrastructure, and increasing reliability concerns for electric utilities. New advances, collectively referred to as the smart grid, include modern electronics, technology, telecommunications, and computing capabilities. Smart grid telecommunication frameworks provide two-way communication to support grid operations. Software-defined networking (SDN) has been proposed as a method of monitoring and controlling telecommunication networks, enabling increased smart grid visibility, control, and security. However, being connected to telecommunications infrastructure means that smart grid networks are exposed to cyber-attacks. Attackers may use unauthorized access to intercept messages, inject false data into system measurements, flood communication channels with false data packets, or target centralized controllers to cripple network control. Defense and security techniques against these threats are constantly evolving, necessitating an up-to-date, comprehensive study analyzing cyber attacks and defense methods for smart grid networks. Previous smart grid security surveys do not contain recent techniques and to our knowledge most, if not all, address only one type of attack type and/or one type of defense. This survey considers the latest security techniques, simultaneous multi-pronged cyber attacks and defensutilityes to meet the challenges of the next-generation SDN smart grid research with the goal of identifying future research needs, describing the open security challenges, and exposing both emerging threats and their potential impact on SD-SG deployment.

The bioindicator potential of seagrass ecosystems in coastal trace metal monitoring is low for tropical island ecosystems. This study evaluated the bioindicator potential of six seagrass species exposed to anthropogenic trace metal pollution in the Andaman and Nicobar Islands (ANI) of India. Sediment and seagrass biomass, samples were analyzed for trace metals (Co, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Zn) from four locations of ANI exposed to anthropogenic contamination. Geo accumulation Index values indicated moderate trace metal contamination in seagrass sediment. Seagrass trace metal accumulation from sediment was both species-specific and location- specific for ANI. Small seagrass species such as H. ovalis, H. beccarii and H. uninervis accumulated the maximum concentration of six trace metals (Cr, Cu, Fe, Ni, Pb, Zn) in their tissues compared to big seagrass species like T. hemprichii and E. acoroides of ANI. This study indicates leaves of small seagrass and roots of big seagrass species can serve as short- and long-term bioindicators respectively, of coastal trace metal contamination in tropical islands of ANI.

Sick leave is influenced by various modifiable lifestyle factors and sex differences. This study examines the impact of body mass index, fruit and vegetable consumption, physical activity, smoking, and alcohol consumption on sick leave among Serbia's working population, focusing on sex differences. Data from the 2019 National Health Survey of Serbia were analyzed, focusing on a representative sample of 4,652 individuals aged 18-65. Chi-square tests and logistic regression models assessed the relationships between lifestyle factors and sick leave, adjusting for sociodemographic, work-related, and health-related variables. Among respondents, 15.8% reported sick leave in the past 12 months, with higher rates among women in both, short-term (13.9% vs. 10.6%) and long-term sick leave (3.4% vs. 2.2%). For men, obesity (OR = 2.6), poor dietary habits (fruit OR = 2.1; vegetables OR = 2.8), smoking (OR = 1.9), and risky alcohol consumption (OR = 4.1) significantly increase the likelihood of sick leave. In women, smoking (OR = 1.8) and risky alcohol consumption (OR = 3.1) are major predictors, while BMI and physical activity show less impact. Fruit and vegetable consumption and physical activity have inconsistent effects on sick leave, indicating the need for further research. This study identifies obesity, poor dietary habits, smoking, and alcohol consumption as key predictors of sick leave in men. In women, smoking and risky alcohol consumption are major predictors. Smoking is a risk factor for both sexes, while risky alcohol consumption emerges as a prominent predictor and is a significant predictor overall. These findings highlight the need for targeted public health interventions to address these lifestyle factors and reduce sick leave rates. Further research is needed to clarify the roles of dietary habits and physical activity in influencing sick leave.

Nasal high-frequency oscillatory ventilation (nHFOV) has emerged as an effective initial and rescue noninvasive respiratory support mode for preterm infants with respiratory distress syndrome (RDS). Multiple noninvasive respiratory support modes are currently available in Romania, but little is known about nHFOV use in Romanian neonatal intensive care units (NICUs). We aimed to identify the usage extent and clinical application of nHFOV in Romania. A structured web-based questionnaire was designed to find the rate of nHFOV use and knowledge of this new method of noninvasive respiratory support in Romanian level III NICUs. The questionnaire was addressed to the leaders of all level III Romanian NICUs. Using multiple-choice, open-ended, and yes/no questions, we collected information on the NICU’s size, noninvasive respiratory support modes used, nHFOV use, indications, settings, nasal interfaces, secondary effects, and equipment used. Descriptive statistics and comparisons were performed using IBM SPSS Statistics 26.0. A total of 21/23 (91.3%) leaders from level III NICUs (median [IQR] number of beds of 10 [10-17.5]) responded to the survey. The most frequently used noninvasive ventilation modes were CPAP mode on mechanical ventilators (76.2%), followed by NIPPV (76.2%), heated, humidified high flow nasal cannula (HHHFNC) (61.9%), and nHFOV (11/21 units; 52.4%); 5/11 units reported frequent nHFOV use (in 2 or more newborns/month) in both term and preterm infants. The main indications reported for nHFOV use were CPAP failure (90.9%), hypercapnia (81.8%), and bronchopulmonary dysplasia (72.7%). 10/11 units reported setting a mean airway pressure (MAP) equal to the CPAP level plus 1-2 cmH₂O on rescue nHFOV after CPAP failure. Both dedicated equipment (45.5%) and hybrid ventilators (63.6%) were used for nHFOV delivery, with face/nasal masks and short binasal prongs being the most commonly used nasal interfaces (90.9% and 72.7%, respectively). Air leaks at the interface level (90.9%), thick secretions (81.8%), and airway obstruction (63.6%) were the most frequently mentioned adverse effects of nHFOV; none of the units mentioned pneumothorax as an adverse effect. Only 3 of the NICUs had a written protocol for nHFOV use. Initial, minimum, and maximum amplitude and initial and minimum MAP settings used were significantly higher with hybrid ventilators as compared to dedicated equipment (p<0.05). Most units not yet using nHFOV cited lack of equipment, experience, training, or insufficient information and evidence for the clinical use and outcome of nHFOV use in neonates as the main reasons for not implementing this noninvasive respiratory mode. Our survey showed that nHFOV is already used in more than half of the Romanian level III NICUs to support term and preterm infants with respiratory distress despite a lack of consensus regarding indications and settings during nHFOV. A need for training for nHFOV use was identified, and efforts are needed at the national level to develop courses and training sessions, including using simulation workshops, to offer more information on optimal nHFOV use in clinical practice.

Background: Pharmacists need up-to-date knowledge and decision-making support in HIV care. We aim to develop MARVIN-Pharma, an adapted artificial intelligence-based chatbot initially for people with HIV, to assist pharmacists, considering evidence-based needs. Methods: From Dec 2022 to Dec 2023, an online needs-assessment survey evaluated Québec pharmacists' knowledge, attitudes, involvement, and barriers relative to HIV care, alongside perceptions relevant to the usability of MARVIN-Pharma. Recruitment involved convenience and snowball sampling, targeting National HIV and Hepatitis Mentoring Program affiliates. Results: Forty-one pharmacists (28 community, 13 hospital-based) across 15 Québec municipalities participated. Participants perceived their HIV knowledge as moderate (M=3.74/6). They held largely favorable attitudes towards providing HIV care (M=4.02/6). They reported a “little” involvement in the delivery of HIV care services (M=2.08/5), most often ART adherence counseling, refilling, and monitoring. The most common barriers reported to HIV care delivery were a lack of time, staff resources, clinical tools, and HIV information/training, with pharmacists at least somewhat agreeing that they experienced each (M≥4.00/6). On average, MARVIN-Pharma's acceptability and compatibility were in the ‘undecided’ range (M=4.34, M=4.13/7, respectively), while pharmacists agreed to their self-efficacy to use online health services (M=5.6/7). Conclusion: MARVIN-Pharma might help address pharmacists' knowledge gaps and barriers to HIV treatment and care, but pharmacist engagement in the chatbot’s development seems vital for its future uptake and usability.

Utilizing groundwater offers a promising solution to alleviate water stress in Ethiopia, providing a dependable and sustainable water source, particularly in regions with limited or unreliable surface water availability. However, effective decision-making regarding well drilling and placement is essential to maximize groundwater resource potential, enhancing agricultural productivity, reducing hunger, and bolstering food security in Ethiopia. This study concentrates on the development of two-stage stochastic mixed integer programming (SMIP) models to optimize well placement for sustainable agricultural irrigation, considering uncertain demand scenarios. Additionally, a deterministic mixed integer programming model is formulated for comparison with the two-stage SMIP. Experiments are conducted to explore various demand scenario distributions, revealing that the optimized total cost for the two-stage SMIP generally exceeds that of a deterministic setting, aligning with the two-stage SMIP's focus on long-term benefits. Moreover, slight differences are observed in well layouts under different assumption scenarios. The study also examines the impact of selected parameters, such as fixed construction costs, per-meter drilling costs, and demand scenarios. The out-of-sample performance shows that the stochastic model is more flexible and resilient, with 11% and 4% lower costs than deterministic cases 1 and 3, respectively. This flexibility provides a more robust long-term strategy for well placement and resource allocation in groundwater management.

Dysmenorrhea symptoms are frequent and often self-treated using non-prescription medicines. To further characterize women with dysmenorrhea using a combination of hyoscine butylbromide and paracetamol (PLUS) for self-management of their complaints, we performed a secondary analysis of a published pharmacy-based patient survey. 314 women (mean age 32.3 years) with dysmenorrhea reported a pain and cramps intensity of 7.45 ± 2.13 on a 0-10 Likert scale, which was reduced to 2.86 ± 1.81 upon treatment. Associated impairments of work/daily chores, leisure activities, and sleep were improved by 64.6%, 62.2% and 70.4%, respectively. The onset of symptom relief was within 60 min in 84.7%. Tolerability was rated as very good or good by 97.2%. These findings confirm the efficacy and tolerability data on PLUS from randomized controlled trials in a larger group of women conducting self-management of their dysmenorrhea.

Perimenopause, the transitional phase leading up to menopause, affects millions of women worldwide, yet remains poorly understood and under-addressed in healthcare. This report investigates the significant impact of perimenopause symptoms on women's lives, emphasizing the often debilitating effects such as anxiety, depression, weight gain, and hot flashes, which collectively cost an estimated $1.8 billion annually in lost work time. Despite the availability of treatment options like Hormone Replacement Therapy (HRT) and non-hormonal alternatives, awareness and utilization of these options vary significantly among women. The report highlights the historical neglect of women's health issues and the need for improved communication between patients and healthcare providers. A cross-sectional survey conducted with 1,000 adults from the United States and Canada reveals widespread dissatisfaction with the quality of healthcare communication, underscoring the importance of personalized and comprehensive insights for women's health. The findings advocate for more at-home solutions and resources to empower women in managing perimenopause and menopause, promoting informed decision-making and reducing stigma. This study aims to enhance awareness and support for women during this critical life stage.

Intrusion Detection Systems (IDS) plays a part in modern cyber security, as a result of the increasing need for cyber security systems in the “real” world due to the increasing number of cyber attacks, more sophisticated systems are required in order to prevent these attacks - an IDS can provide this protection. Due to the sophistication of these systems, they must be properly understood, developed and analyzed - research papers can be used as a tool to improve IDS systems. This paper is composed of two main sections: a survey and a taxonomy, providing information, reviews and interpretations from relevant papers, a timeline of important papers, a discussion on the future of IDS and a classification on IDS and how to apply this.

In the age of next-generation computer, the role of the cloud, the internet and smart devices will become stronger. These days we all know the word smart well. This word is often used in our daily lives. The Internet of Things (IoT) will generate a variety of information from a variety of resources. It can store big data in the cloud. Fog computing acts as a signal between cloud and IoT. Fog extensions in this framework apply to material under IoT. IoT devices are called Fog nodes, which can be accessed anywhere within the network range. A blockchain is a novel way of recording in a secure sequence. Creating a new framework in the development of Internet of Things is one of the critical problems of wireless communication where solving such a problem can lead to continued growth in the use and popularity of IoT. Proposed research creates a framework for providing a framework for middleware on the internet of smart devices network for the internet of things using blockchains technology. Our great offering connects new research that integrates blockchains into the Internet of Things and provides secure Internet connection for smart devices. Blockchain (BC) Internet of Things (IoT) is a new technology that works with low-level, distributed, public and real-time leaders to maintain transactions between IoT sites. A blockchain is a series of blocks, each block being linked to its previous blocks. All blocks have cryptographic hash code, previous block hash, and its data. Transactions in BC are the basic components used to transfer data between IoT nodes. IoT nodes are a variety of portable but smart devices with embedded sensors, actuators, systems and the ability to communicate with other IoT nodes. The role of BC in IoT is to provide a process for processing secure data records using IoT nodes. BC is a protected technology that can be used publicly and openly. IoT requires this type of technology to allow secure communication between IoT nodes in different environments. Events in BC can be tracked and monitored by anyone who is certified to communicate within IoT.

Back-care knowledge and behaviour may play a vital role in preventing musculoskeletal current and future discomfort and injuries. The aim of the current survey was to explore the level of knowledge regarding ergonomics and posture together with postural behaviour and the incidence of musculoskeletal pain (MSP) in university students in the northeast of England. A mixed methods survey was conducted on a convenience sample of 55 university students. The 32-item valid and reliable questionnaire contains questions on socio-demographics, MSP, knowledge regarding ergonomics and posture (8 closed ended and 8 open-ended items) as well as postural behaviour. Nearly 1.8%, 20% and 78.2% of the university students had poor, fair and good knowledge of ergonomics and posture respectively. Among the 37 students who carried a backpack, 51.4% were found to have poor postural behaviour, 37.8% had fair behaviour and 10.8% had good postural behaviour. Of the 18 students who did not carry a backpack, 55.6% were found to have poor postural behaviour, 27.8% had fair behaviour and 16.6% had good postural behaviour. 70.9% of the students experienced MSP with the most prevalent site of pain being the lower back region. The key themes that emerged from qualitative data (8 open-ended questions) were: “Comfortable posture”, “Correct spinal alignment”, “Spine support”, “less pain and injuries”, “Use knees not the back”, “Keep the object close to body”, “Hands on desk”, “Feet flat on floor” as well as “eyes at screen level”. The findings of this study can be used to develop targeted interventions aimed at educating university students about ergonomics and posture in order to prevent the incidence of MSP. Future research in this field with a large and more representative sample of the university students’ population is strongly recommended to fully understand their knowledge of ergonomics and posture, postural behaviour as well as incidence of MSP.

Common prosperity is the core goal of rural revitalization, and sustainable growth of farmers' income is an inevitable requirement. This study uses CLDS2016-2020 village and family level questionnaire data to reveal how the degree of village organization affects the sustainable growth of farmers' income, thus providing reference and decision-making for the local government to promote the increase of farmers' income and narrow the gap from the village level. The results show that the degree of village organization can significantly affect farmers' income, and the analysis results of the degree of formal organization are consistent with them. Specifically, for every 1% increase in the degree of village organization, the per capita household income will increase by 32.4%, among which the wage income will increase by 22.6%, the operational income will increase by 5%, the property income will increase by 0.8%, and the transfer income will increase by 4.2%. The estimation results through instrumental variables are consistent, and the results are robust. At the same time, improving the degree of village organization can also significantly increase the proportion of farmers' non-agricultural income and narrow the income gap among farmers. On the impact of heterogeneity, it is found that the geographical distance between villages and county and township governments, as well as the political identity and education level of farmers will affect the role of the degree of organization. The results of mechanism analysis show that improving the degree of village organization is mainly through promoting the circulation of agricultural land, socialized service of agricultural machinery and industrial and commercial capital to the countryside to increase farmers' income. The research revealed that the modernization of rural governance should be accelerated, and the degree of village organization should be improved, especially the degree of formal organization, so as to build a benign trust and cooperation relationship between the two village committees and the villagers, and implement policies and measures to improve the income of farmers according to local conditions. At the same time, strengthen the publicity and education in party member to improve the proportion of rural party member and the education level of farmers.

Aims: To investigate the road safety concerns associated with pet dogs in New Zealand. Methods: An online survey was developed and offered to New Zealanders via SurveyMonkey© from 8 January 2019 to 31 March 2019) using New Zealand residents aged ≥18 years. Questions about the demographics of respondents and their number of pet dogs along with those about dog transportation and dog roaming were asked. Results: Of 2744 respondents who completed the online survey, 1494 (65%) owned a dog. Of these, 1511 completed the question about how their dog or dogs travel in vehicles. Of these, 2 % (n = 29) let their dog sit on the driver's knee, and 12 % (n = 179) allowed their dog to sit on the passenger's knee. Only 7 % (n = 105) allowed the dog to roam free throughout the vehicle, while 44% (n = 663) allowed their dogs to roam free in the backseat area only and 19% (n = 280) put their dog in the boot/hatchback area. A minority of respondents (2%, n = 22) indicated they let their dog travel unrestrained on the deck of a ute or truck. Approximately half, 51% (n = 767) of the respondents, indicated that they restrained or crated their dogs in or on the vehicle. Respondents also expressed concerns about roaming dogs being a road safety hazard with the themes of responsible ownership, physical and psychological harm, and the dangers of rescue altruism emerging. Conclusions: Increased awareness about proper pet restraints during transportation and preventing dogs from roaming, especially near and on roads, is crucial to ensure traffic safety for humans and dogs.

Low fertility is a critical social problem worldwide, and infertility has a prevalence of 15%. This cross-sectional study aimed to understand the factors affecting the usage and satisfaction of Korean medicine (KM) in subfertile women. An online survey was conducted from November 3 to November 8, 2021. The survey collected basic information, KM treatment experience, and satisfaction from women who experienced poor pregnancy. The t-test and chi-square test (χ2-test) were used to determine the overall characteristics of the subjects and factors affecting the utilization and satisfaction of KM treatment. Of the total of 29,465 people, 4,922 read the survey email, and 601 responded. After excluding 51 insincere respondents, 550 respondents were included in the final analysis. Of these, 43.1% had experience with conventional treatment, and 15.6% had received KM treatment. Integrative subfertility treatment was significantly more common (24.9%) than KM treatment alone (10.2%) (P=0.00). Women with no childbirth experience (P=0.01) and those with a master’s degree (P=0.01) were more willing to participate in the ‘KM Support Project for Subfertility’. Our findings suggest that subfertile patients prefer integrated medical treatment over KM alone. Further studies are needed to assess the status of integrative medicine treatment, satisfaction with each KM intervention, factors for low satisfaction, and patient requirements.

Depressive disorders represent the leading contributors to mental health-related global burden, and they are often diagnosed in the context of many comorbid disorders, such as cardiovascular disorders, Stroke, Parkinson’s Disease, Major Neurocognitive Disorders and Headache, thus worsening their outcomes. Depression is a challenge for “real-world” clinicians, due to the low rates of remission despite the increasing number of antidepressant strategies currently available. Indeed, current antidepressant strategies often fail to achieve acceptable rates of remission. One of the main challenges of “real-world” management of depression is represented by geriatric depression, which is common and under diagnosed. The challenge of diagnosis and treatment of geriatric depression in real world calls for the need of a deeper exploration of its management in clinical practice. This is the purpose of the present cross-sectional survey, aimed at evaluating the clinical approach to late-life depression in a sample of expert physicians working in geriatric settings in Italy, thus trying to provide useful insights on geriatric depression care.

Driven by mounting environmental apprehensions stemming from the over dependence on fossil fuels in energy and transportation sectors, extensive exploration into alternative energy sources, particularly biomass, has stimulated profound research on harnessing the potential of computational and mathematical techniques. This comprehensive survey delves into optimal models and strategies, unveiling their pivotal role in the design of biomass gasification processes.of hydrogen through biomass gasification. In this study, we aim to provide a comprehensive overview of the computational and mathematical techniques employed in optimizing biomass gasification processes, with a specific focus on enhancing hydrogen yield. Through an extensive literature review, various models and strategies will be examined, including thermodynamic analysis, kinetic modeling, reactor design, and process optimization. By uncovering the power of these techniques, we aim to contribute to the advancement of sustainable and efficient biomass utilization for the hydrogen-based future economy.This paper aims to provide an updated and comprehensive coverage of the investigations conducted on the potential of producing hydrogen from biomass through gasification. To achieve this, we incorporate the latest works that have utilized numerical modeling, simulation, optimization techniques, process heat integration, and co-generation in their studies. By encompassing these aspects, we aim to offer a broader and more in-depth re-appraisal of the subject matter. This will ensure that readers gain a holistic understanding of the advances made in the field and the potential for sustainable hydrogen production of biomass gasification.Through a meticulous re-appraisal and analysis of each subject, we can identify their respective strengths and areas that require further research effort. By thoroughly examining numerical modeling, simulation, optimization techniques, process heat integration, and co-generation, we can assess their effectiveness and applicability in the context of biomass gasification for hydrogen production. This analysis shed light on the areas where these techniques excel, as well as pinpoint limitations or gaps in current understanding. By identifying these areas, we can highlight the need for further research and development, enabling us to make significant advancements in biomass gasification processes and pave the way for a sustainable and efficient hydrogen-based future economy..

Drug discovery and development is a highly complex and costly endeavor, typically requiring over a decade and substantial financial investment to bring a new drug to market. Traditional computer-aided drug design (CADD) has made significant progress in accelerating this process, but the development of quantum computing offers potential due to its unique capabilities. This paper discusses the integration of quantum computing into drug discovery and development, focusing on how quantum technologies might accelerate and enhance various stages of the drug development cycle. Specifically, we explore the application of quantum computing in addressing challenges related to drug discovery, such as molecular simulation and the prediction of drug-target interactions, as well as the optimization of clinical trial outcomes. By leveraging the inherent capabilities of quantum computing, we might be able to reduce the time and cost associated with bringing new drugs to market, ultimately benefiting public health.

Hybrid methods are a suitable option to extract dietary patterns associated with health outcomes. This study aimed to identify dietary patterns of Brazilian adults (20-59 years old; n=28,153) related to dietary components associated with the risk of obesity. Data from the 2017-2018 Brazilian National Dietary Survey were analyzed. Food consumption was obtained through 24-hour recall. Dietary patterns were extracted using partial least squares regression. The selected response variables were energy density (ED), percentage of total fat (%TF), and fiber density (FD). In addition, 32 food groups were established as predictor variables. The first dietary pattern, named as energy-dense and low-fiber (ED-LF), included with positive factor loadings: solid fats, breads, added-sugar beverages, fast foods, sauces, pasta, and cheeses, and with negative factor loadings: rice, beans, vegetables, water, and fruits. Higher adherence to the ED-LF dietary pattern was observed for individuals >40 years old, from urban areas, in the highest income level, who were not on a diet, reporting away-from-home food consumption, and having ≥1 snack/day. Eating patterns with similar characteristics are often associated with an increased risk of obesity. The results are consistent with recommendations to increase the consumption of fresh foods and to reduce ultra-processed products.

Text mining is a technological trend often highlighted in the continuous exchange of information through interconnected media. Its applicability goes beyond private organizations, as the public sector also requires it to treat textual information regarding services offered. Within this scenario, public security emerges as a prominent user of text mining that seeks to ensure the construction of data and knowledgebases to support decision-making about law enforcement actions to ensure citizen welfare. The primary objectives of this article are: (i) to develop a survey to identify text mining applications, techniques, opportunities, and challenges in public security, and (ii) to outline research directions concerning these topics and provide insights so that interested researchers can develop new studies. The literature was searched within four databases: Scopus, IEEE Xplore, ACM Digital Library, and Web of Science. A filtering process was applied to extract the works most aligned with the target theme, resulting in the selection of 194 of the most relevant works for a literature review. There were identified nineteen key applications of text mining related to public security and the most recurrent techniques and technologies reported between 2014 to 2021, supporting outlining three axes for future directions: one with possible expansion of objectives for new research; another on changes and adaptations in scopes for the methodological context; and the last one on expansions and changes in application scenarios based on the literature.

In this paper, the results of a high-precision control survey carried out in the underground mining of the Oyu Tolgoi project using terrestrial laser scanning technology are presented. The purpose of the measurements was to ensure that the direction, total length and volume of the penetration and excavation works were being implemented in accordance with the planned and designed dimensions. The coordinates of the geodetic traverse checkpoints were also assessed. In addition, we checked a traverse network of 18 checkpoints in the tunnel of the selected underground level. These checkpoints were repeatedly measured four times by two using readings of the Trimble S7 1” total station and were processed using the Compass/Bowditch method with Trimble Business Center software. All points except for one were within the tolerance limits. Based on these points, the excavation length and volumes were determined using the terrestrial laser scanners (TLSs) Trimble X7 and Trimble SX12 [25]. The difference between the planned work quantity provided by the Oyu Tolgoi project and the actual values measured using laser scanner was calculated with the comparative method. The TLS partial control measurements in the excavations made through the ore bodies revealed a length of 20285.72m and a volume of 441128.80m3 for the seven underground mine levels, while their project values were 16158.26m length and 422837.50m3 volume.

The Tonle Sap Lake (TSL) landscape is a region of vast natural resources and biological diversity in the heart of Southeast Asia. In addition to serving as the foundation for a highly productive fisheries system, this landscape is home to numerous globally threatened species. Despite recognition by several governmental and international agencies for decades, nine protected areas have been established within this region, natural landcover such as grasslands have experienced considerable declines since the turn of the century. This project used local expert knowledge to train and validate a random forest supervised classification of Landsat satellite imagery in Google Earth Engine. The time series of thematic maps was then used to quantify the conversion of grasslands to croplands between 2004 and 2023. The classification encompassed a 10-kilometer buffer surrounding the landscape, an area of nearly 3 million hectares. The average overall accuracy for these thematic maps was 82.5% (78.5% - 87.9%), with grasslands averaging a 76.1% user’s accuracy. The change detection indicated that over 207,281 ha of grasslands were lost over this period (&gt; 59.5% of the 2004 area), with approx. 89.3% of this loss could be attributed to cropland expansion. The results of this project will inform conservation efforts focused on local scale planning and management of commercial agriculture.

Federated learning (FL) has become a burgeoning and attractive research area, which provides a collaborative training scheme for distributed data sources with privacy concerns. Most existing FL studies focus on taking unimodal data, such as images and text, as the model input and resolving the heterogeneity challenge, i.e., the non-identically distributed (non-IID) challenge caused by data distribution imbalance related to data labels and data amount. In real-world applications, data are usually described by multiple modalities. However, to the best of our knowledge, only a handful of studies have been proposed to improve the system performance by utilizing multimodal data. In this survey paper, we identify the significance of this emerging research topic – multimodal federated learning (MFL) and perform a literature review on the state-of-art MFL methods. Furthermore, we categorize multi-modal federated learning into congruent and incongruent multimodal federated learning based on whether all clients possess the same modal combinations. We investigate the feasible application tasks and related benchmarks for MFL. Lastly, we summarize the promising directions and fundamental challenges in this field for future research.

Video editing is a high-required job, for it requires skilled artists or workers equipped with plentiful physical strength and multidisciplinary knowledge, such as cinematography, aesthetics. Thus gradually, more and more researches focus on proposing semi-automatical and even fully automatical solutions to reduce workloads. Since those conventional methods are usually designed to follow some simple guidelines, they lack flexibility and capability to learn complex ones. Fortunately, the advances of computer vision and machine learning make up the shortages of traditional approaches and make AI editing feasible. There is no survey to conclude those emerging researches yet. This paper summaries the development history of automatic video editing, and especially the applications of AI in partial and full workflows. We emphasizes video editing and discuss related works from multiple aspects: modality, type of input videos, methology, optimization, dataset, and evaluation metric. Besides, we also summarize the progresses in image editing domain, i.e., style transferring, retargeting, and colorization, and seek for the possibility to transfer those techniques to video domain. Finally, we give a brief conclusion about this survey and explore some open problems.

In order to be able to face competition, thriving corporations have to be compelled to maintain an awfully smart relationship with their existing customers and additionally to be able to anticipate their future wants. Thus, corporations do not target customers as teams, however they’re attempting to focus on them as individuals. However, to be able to use this information, corporations have to be compelled to use promoting automation tools. Marketing Automation was an idea first introduced in 2001 by John D.C. Little in his presentation at the 5th Invitational Choice Symposium UC Berkeley 2001. This survey paper assesses prominent research on Marketing Automation and suggests how it can be modified to adapt to the current marketing scenario.

Diffusion Models (DMs) are probabilistic models that create realistic samples by simulating the diffusion process, gradually adding and removing noise from data. These models have gained popularity in domains such as image processing, speech synthesis, and natural language processing due to their ability to produce high-quality samples. As DMs are being adopted in various domains, existing literature reviews that often focus on specific areas like computer vision or medical imaging may not serve a broader audience across multiple fields. Therefore, this review presents a comprehensive overview of DMs, covering their theoretical foundations and algorithmic innovations. We highlight their applications in diverse areas such as media quality, authenticity, synthesis, image transformation, healthcare, and more. By consolidating current knowledge and identifying emerging trends, this review aims to facilitate a deeper understanding and broader adoption of DMs and provide guidelines for future researchers and practitioners across diverse disciplines.

In recent years, cryptocurrencies have received substantial attention from investors, researchers and the media due to their volatile behaviour and potential for high returns. This interest has led to an expanding body of research aimed at predicting cryptocurrency prices, which are notably influenced by a wide array of technical, sentimental, and legal factors. This paper reviews scholarly content from 2014 to 2024, employing a systematic approach to explore advanced quantitative methods for cryptocurrency price prediction. It encompasses a broad spectrum of predictive models, from early statistical analyses to sophisticated machine and deep learning algorithms. Notably, this review identifies and discusses the integration of emerging technologies such as Transformers and hybrid deep learning models, which offer new avenues for enhancing prediction accuracy and practical applicability in real-world scenarios. By thoroughly investigating various methodologies and parameters influencing cryptocurrency price predictions, including market sentiment, technical indicators, and blockchain features, this review highlights the field’s complexity and rapid evolution. The analysis identifies significant research gaps and under-explored areas, providing a foundational guideline for future studies. These guidelines aim to connect theoretical advancements with practical, profit-driven applications in cryptocurrency trading, ensuring that future research is both innovative and applicable.

The rapid integration of Information and Communication Technology (ICT) is transforming the traditional electrical grid into a {\em Smart Grid}. Smart grids enable two-way communication and improved monitoring and control between utilities and customers. However, due to its heterogeneous nature, public exposure, and weak security at low-powered devices, the Smart Grid has vulnerabilities to various malicious threats, adversaries, and cyber attacks, which may affect cost and service availability. Additionally, when the systems' confidentiality, integrity, or availability are compromised, the resulting fallout can threaten national security and have cascading effects on human lives. Given the extreme consequences of an attack, smart-grid technology must be thoroughly tested for correct operation and security {\em before} it is deployed. As a result, vulnerability testing of smart grids, not only for correctness but for security purposes, has been the subject of numerous studies by academics, government agencies, and private companies. This paper reviews the vulnerabilities associated with the smart grid and spotlights simulation as the vulnerability testing methodology conducted in recent pertinent research works. It also presents various security aspects of the smart grid, including grid applications, system and network infrastructure and components, cyber threats and attacks, simulation, and different mitigation techniques. Finally, we analyze the gaps in the current research works, focusing on simulation. We briefly present a real-time simulation testbed that mimics customer behaviour and integrates hardware in the loop to apply attack methods, analyze vulnerabilities and risk mitigation associated with the smart grid system, and propose future work to improve the current framework.

The emergence of diverse infections worldwide, which is a serious global threat to human existence, necessitate the urgent development of novel therapeutic candidates that can combat these diseases with efficacy. Isatin, a secondary metabolite from tryptophan is considered a privileged scaffold and favorable pharmacophore with a unique structural moiety and varied chemical and biological properties resulting in its widespread applications by medicinal chemists. Molecular hybridization is one of the most common and important techniques use in the synthesis of their bioactive hybrids in drug discovery. This review containing published articles from 2005 to 2022 focuses on isatin hybrids which have been synthesized and reported in the literature alongside a discussion on their biological properties. The purpose of this review is to set up the direction for the design and development of isatin-hybrids with tailored biological properties as effective therapeutic candidates inspired by nature.

Autonomous Driving (AD) has become a prominent research area in the field of Artificial Intelligence (AI) and Machine Learning (ML) in recent years. This opens the door wider for self-driving cars to surpass conventional vehicles in the current market share. Despite its apparent simplicity, AD is composed of complex and heterogeneous systems, which are in need of a high level of coordination and alignment to ensure both full automation and safety. Therefore, numerous research studies have been conducted over the last few years to facilitate such coordination and accelerate the capacity of these types of vehicles to be self-managed in complex situations. The paper summarises these approaches that led to building what is known nowadays as the autonomous driving pipeline. Moreover, although skepticism exists regarding the practicality of AD as a viable alternative to traditional vehicles, extensive research suggests the multiple benefits of relying on them for mobility. While challenges remain in implementing AD in the real world, including regulation and technical issues, substantial progress in recent years indicates a growing acceptance of AD in the near future. This paper further explores the advantages and opportunities of the conducted such systems in facilitating the practicality of Autonomous Driving.

Image registration (IR) is a process that deforms images to align them with respect to a reference space, making it easier for medical practitioners to examine various medical images in a standardized reference frame, such as having the same rotation and scale. This document introduces image registration using a simple numeric example. It provides a definition of image registration along with a space-oriented symbolic representation. This review covers various aspects of image transformations, including affine, deformable, invertible, and bidirectional transformations, as well as medical image registration algorithms such as Voxelmorph, Demons, SyN, Iterative Closest Point, and SynthMorph. It also explores atlas-based registration and multistage image registration techniques, including coarse-fine and pyramid approaches. Furthermore, this survey paper discusses medical image registration taxonomies, datasets, evaluation measures, such as correlation-based metrics, segmentation-based metrics, processing time, and model size. It also explores applications in image-guided surgery, motion tracking, and tumor diagnosis. Finally, the document addresses future research directions, including the further development of transformers.

Background: Public misconception and demand for the indication of antibiotics could lead to inappropriate prescribing and consumption. Successful treatment can only be achieved when the public and industrial users have knowledge on antibiotic use and resistance. This survey is aimed to assess antibiotic usage and knowledge regarding antibiotics and antimicrobial resistance (AMR) among undergraduate students of Universiti Brunei Darussalam (UBD), public university located in Brunei Darussalam. Methods: A cross-sectional study was performed using a self-administered questionnaire. The questionnaire was adapted from the World Health Organization (WHO) Antibiotic Resistance, Multi-country public awareness survey distributed online. Students at UBD were invited to participate in the online survey through internal email. The questionnaire consisted of 5 sections: demographic information, antibiotic usage, knowledge on antibiotics, antibiotic resistance (AMR) and use of antibiotics in agriculture. Data were analyzed descriptively and appropriate inferential statistics was used accordingly. Cronbach’s alpha was also done to determine the internal consistency. The section on antibiotic use and knowledge showed good internal consistency of Cronbach’s alpha 0.66 and 0.86 respectively. Research ethics approval was obtained from the PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam (UBD). Results: A total of 145 students returned the complete questionnaire. The result of the study found that 50% of the students had good level of knowledge of antibiotic and antimicrobial resistance with a mean total knowledge score of 11.4 out of 14. Respondents reported the use of antibiotic in the past (69%). Many of the students could identify the use of antibiotics for the treatment of bacterial infection. However, there were also students who incorrectly thought that antibiotics can be used for cold and flu (43%) and fever (41%). Moreover, 76% of the respondents mistakenly believed that antibiotic resistance is the result of the body becoming resistant to antibiotics. Only 12% of the respondents were found to have poor knowledge in the study. Conclusions: Misconceptions in regards to the use of antibiotics for conditions related to viral illnesses was noticed among the respondents in our study. Thus, improving knowledge on antibiotics is crucial to address those beliefs.

The development of the Internet of Things (IoT) has triggered a virtual wave of interconnection and intercommunication among an enormous number of universal things. This has caused an exceptional surge of colossal heterogeneous information, known as an information explosion. Until now, cloud computing has filled in as a proficient method to process and store these data. Still, it came to light that by utilizing just cloud computing, pesky issues like, the expanding requests of actual-time or speed-sensitive applications and the restrictions on system transfer speed could not be solved. Consequently, another computing platform, called fog computing has been advanced as a supplement to the cloud arrangement. Fog computing spreads the cloud administrations and services to the edge of the system, and brings processing, communications and reserving and storage capacity closer to edge gadgets and end-clients and, in the process, aims at enhancing versatility, low latency, transfer speed and safety and protection. This paper takes an extensive and wide-ranging view of fog computing, covering several aspects. At the outset is outlined the many-layered structural design of fog computing and its attributes. After that, chief advances like communication and inter-exchange, computing, reserving and storage, asset administration, naming, safety and safeguarding of privacy are delineated while showing how this backup and facilitate the installations and various applications. Then, numerous applications like augmented reality (AR), healthcare, gaming and brain-machine interface, vehicular computing, smart scenarios etc. are highlighted to explain the fog computing application milieu. Following that, it is shown that how, despite fog computing being a features-rich platform, it is dogged by its susceptibility to several security, privacy and safety concerns, which stem from the nature of its widely distributed and open architecture. Finally, some suggestions are advanced to address some of the safety challenges discussed so as to propel the further growth of fog computing.

The escalating demand for multifunctional and advanced materials for growing industries has turned scientific exploration to graphene-based hybrid filler polymer composites. The introduction of graphene with other fillers into polymer matrices results in astonishing properties and thus has gained significant attention in recent years. This study addresses the significance of graphene-based hybrid filler polymer composites in progressive materials science and engineering. The paper encloses some traditional and current preparation techniques employed in the fabrication of composites and a comprehensive review of successfully prepared graphene hybrid polymer composites with enhanced mechanical, thermal, and electrical properties reported in published research. The role of different factors, e.g., filler type, aspect ratios, filler-matrix compatibility, dispersion effects, homogeneity, filler orientation through polymer matrices, and curing conditions, are summarized. Finally, the applications in heavy industries, e.g., aerospace, electronics, energy storage, and automotive sectors were also analyzed. We hope this study will serve as a practical resource for new researchers and practitioners to understand and harness the full potential of these composites in various scientific and industrial fields.

Zoo conditions are unique for reindeer, since even domestic reindeer are not kept in captive facilities like cattle. In the zoo, reindeer are usually surrounded by many different animals they would never encounter naturally. Thus they might pick up some new helminths. Numerous petting zoos raise an issue of safety of tactile communications for human visitors. Out study is the first large-scale one. Qualitative and quantitative fecal analysis was carried out for 233 reindeer distributed over 50 Russian zoos according to National Standard of the Russian Federation (GOST R 54627-2011) Ruminant animals—Methods of Laboratory Helminthological Diagnostics. Where possible, DNA analyses of helminths were performed targeting internal transcribed spacer region. In result, F. hepatica, Paramphistomum sp., Moniezia sp. (including M. expansa), gastrointestinal strongylids (including Nematodirus spp.), Dictyocaulus sp., E. rangiferi, Trichuris sp., and Capillaria sp. were found in 106 (45%) zoo reindeer. All these helminths were previously reported for reindeer and pose no direct danger for humans. Intensity of invasions was mostly low. Fecal examination might be considered as an indirect method for mange diagnostics, as Chorioptes and Demodex mites were found in reindeer fecal samples. The latter may represent a novel species of mite specific for reindeer.

Several studies show that air pollution has become a major problem and a source of great concern. Part of the observed poor air quality can be attributed to the levels of ultrafine particles (UFP), generally defined as the fraction of particles with diameter less than 100 nm which, by their size, are able to enter the blood circulation causing adverse health effects. As the main route of entry into the body is inhalation, several epidemiological studies associate the presence of UFP to total mortality, cardiovascular and respiratory problems. Since there are apparently no worldwide normative references for the control of these particles, this global study aimed to characterise the methods and techniques used worldwide regarding the monitoring and management of UFP. For this purpose, a survey was carried out based on a questionnaire applied at global scale. The questionnaire design was based on a preliminary literature review on the main search engines for scientific publications to establish the state of the art. As a result of the application of the questionnaire, a total of 51 responses were obtained from 20 different countries on five continents, which provided an insight into the global profile of professionals interested in the control/study of UFP, the measurement methodologies they follow and the guideline values they apply, if any. Among them are public health professionals, academics and health engineers from various institutions, such as education, hospitals, research centers and regulators in the health sector. Most responses said are no specific methodologies for the measurement of UFP, nor national standards for it. Through the results obtained in this study, it can be concluded that it is urgent to adopt regulations to standardize the assessments as well as to establish guide values for the protection of the human health.

significant security threats to Ransomware attacks provide serious security hazards to personal and corporate data and information. The owners of computer-based resources suffer serious verification and privacy violations, monetary losses, and reputational damage due to a successful ransomware assault. As a result, it is reported critically, accurately, and swiftly identifying ransomware. Numerous methods have been proposed for ransomware, each with pros and cons. The main objective of this study is to discuss current trends and potential future debates on automated ransomware detection. The document includes an overview of ransomware, a timeline of assaults, and details on their background. It also provides a comprehensive study of existing methods for identifying, avoiding, minimizing, and recovering from ransomware. An analysis of studies between 2017 and 2022 is another advantage of the study. This provides readers with up-to-date knowledge of the most recent developments in ransomware detection. It also highlights advancements in methods for combating ransomware attacks. In conclusion, this study highlights unanswered concerns and potential research challenges in ransomware detection.

Urban air quality is increasingly becoming a cause for concern for the health of the human population. The poor air quality is already wreaking havoc in major cities of the world, where serious health issues and reduction of average human life by a factor of years are reported. The air quality in developing countries can become worse as they undergo development. The urban air quality varies non-linearly depending upon the various factors such as land use, industrialization, waste disposal, traffic volume, etc. To address this problem, it is necessary to look at the plethora of available literature from multiple perspectives such as types and sources of pollutants, meteorology, urban mobility, urban planning and development, health care, economics, etc. In this paper, we provide a comprehensive survey of the state-of-the-art in urban air quality. We first review the fundamental background on air quality and present the emerging landscape of urban air quality. We then explore the available literature from multiple urban air quality measurement projects and provides the insights uncovered in them. We then take a look at the sources that are significantly contributing to polluting the air quality. Finally, we highlight open issues and research challenges in dealing with urban air pollution.

Over the last decade, the field of Human Attribute Recognition (HAR) has dramatically changed, mainly due to the improvements brought by deep learning solutions. This survey reviews the progress obtained in HAR, considering the transition from the traditional hand-crafted to deep-learning approaches. The most relevant works on the field are analyzed concerning the advances proposed to address the HAR's typical challenges. Furthermore, we outline the applications and typical evaluation metrics used in the HAR context. Finally, we provide a comprehensive review of the publicly available datasets for the development and evaluation of novel HAR approaches.

Developmental, clinical, and epidemiological research have demonstrated the salience of perceived racial discrimination (PRD) as a contributor to negative mental health outcomes in adolescence. This article summarizes secondary analyses of cross-sectional data from a large-scale youth survey within a predominantly rural state to estimate the prevalence and strength of association between PRD and serious psychological distress, suicidal ideation, and prior suicidal attempts. Data from 93,812 students enrolled in Grades 6, 8, 10, and 12 within 129 school districts across Kentucky were examined to determine prevalence rates for subgroups within the cohort. Logistic regression analyses assessed differences and established comparative strength of association among these variables for racial/ethnic subgroups. PRD was self-reported at high rates across several demographic subgroups and was most evident among Black (24.5%) and Asian (22.1%) students. Multiracial students experienced the highest rates of both SPD and suicidality. Both for the entire cohort and for each racial/ethnic subgroup, PRD was significantly associated with increased likelihood of negative mental health outcomes, although the strength of these associations varied across subgroups and developmental levels. Implications for early intervention and prevention are discussed.