The availability and sophistication degree of such services are fair measures of progress for any city. In this context, Geographic information systems " GIS " offers solutions that support the decision - making processes regarding management, planning and distribution of services, ultimately improving the standard of living in cities (Aziz, 2007, p. 11). Investigating services planning standards is one of the most relevant issues concerning human progress regarding its proper definition and needs. Planning standards can be reconsidered by studying the variation in the distribution of geographical phenomena and the characteristi cs of geographic areas. More effort should be exerted in defining these standards parallel to the characteristics of each region. Such efforts will facilitate appropriate allocation s of services and accurate definitions of future developmental efforts. The problem of the study is that the planning standards are not suitable for the characteristics of the Egyptian cities, which include more population and intensive daily use of services. The solution to this problem is to create new planning standards that suit the rapidly changing nature of cities, and to generate these criteria current services and their intensity and the built - up areas are going to be used to reflect the characteristics of the city, taking this abroach is a new way to generate such criteria. This study attempts to derive planning standards for public services in the city of Qena that are compatible with the characteristics of the city, the geographical distribution of the population, the built - up area, and the services therein.

Lung cancer (LC) ranks among the most frequently diagnosed cancers and is one of the most common causes of death for men and women worldwide. Computed Tomography (CT) images are the most preferred diagnosis method because of their low cost and their faster processing times. Many researchers have proposed various ways of identifying lung cancer using CT images. However, such techniques suffer from significant false positives, leading to low accuracy. The fundamental reason results from employing a small and imbalanced dataset. This paper introduces an innovative approach for LC detection and classification from CT images based on the DenseNet201 model. Our approach comprises several advanced methods such as Focal Loss, data augmentation, and regularization to overcome the imbalanced data issue and overfitting challenge. The findings show the appropriateness of the proposal, attaining a promising performance of 98.95% accuracy.

The Israeli army has demolished several Palestinian homes in the Jenin refugee camp as it continues the deadly raids across the occupied West Bank that it launched on January 21. Explosions echoed throughout the camp overnight as Israeli forces demolished the civilian homes, Wafa, the official Palestinian news agency, reported on Friday. Witnesses said Israeli forces reinforced their presence around the camp and conducted intensive drone surveillance. The army also continues to besiege Jenin Governmental Hospital, having bulldozed the main entrance and the main road leading to it earlier in its raids. Earlier this week, it carried out the demolition of residential blocks in Jenin for the first time since 2002, as reported by Jenin Governor Kamal Abu al-Rub.

An Israeli drone attack on a vehicle near the occupied West Bank town of Qabatiya has killed two people, the Palestinian Ministry of Health says on the fourth day of a large-scale Israeli operation in and around the nearby city of Jenin. The Israeli military said the air attack on Friday in the Jenin governorate hit a vehicle with what it said was a "terrorist cell" inside, but it gave no further details. The official Palestinian news agency Wafa reported that it was a drone attack that happened just before Israeli forces stormed Qabatiya and began "sweeping operations". The air attack coincided with the ongoing military operation against Palestinian fighters in Jenin and its adjacent refugee camp, which has already resulted in the deaths of 14 Palestinians and injured about 50 others, according to the Palestinian Health Ministry in Ramallah. The Israeli military also announced the arrests of 20 people it considers "wanted suspects" and said it had seized weapons.

We introduce SinaTools, an open-source Python package for Arabic natural language processing and understanding. SinaTools is a unified package allowing people to integrate it into their system workflow, offering solutions for various tasks such as flat and nested Named Entity Recognition (NER), fully-flagged Word Sense Disambiguation (WSD), Semantic Relatedness, Synonymy Extractions and Evaluation, Lemmatization, Part-of-speech Tagging, Root Tagging, and additional helper utilities such as corpus processing, text stripping methods, and diacritic-aware word matching. This paper presents SinaTools and its benchmarking results, demonstrating that SinaTools outperforms all similar tools on the aforementioned tasks, such as Flat NER (87.33%), Nested NER (89.42%), WSD (82.63%), Semantic Relatedness (0.49 Spearman rank), Lemmatization (90.5%), POS tagging (97.5%), among others. SinaTools can be downloaded from (https://sina.birzeit.edu/sinatools).

An Israeli air attack on a car in the occupied West Bank city of Tubas has killed at least five Palestinians and left others wounded, local media and medical workers report. Israel said early on Thursday morning that military aircraft took part in "three different attacks" on Palestinian fighters who "posed a threat" to their forces in the Tubas region. Palestinian medics reported that five people were killed in an Israeli air strike targeting a car and two people were injured, one of whom was critical, according to the Palestinian state news agency, Wafa. The Reuters news agency later reported that the number of those killed in the drone attack had risen to at least six. According to Wafa, the attack was carried out by an armed drone, and Palestine Red Crescent Society ambulance crews brought the bodies of the five deceased men and two wounded people to the Tubas Turkish Government Hospital.

This paper addresses the challenge of scaling Large Multimodal Models (LMMs) to expansive 3D environments. Solving this open problem is especially relevant for robot deployment in many first-responder scenarios, such as search-and-rescue missions that cover vast spaces. The use of LMMs in these settings is currently hampered by the strict context windows that limit the LMM's input size. We therefore introduce a novel approach that utilizes a datagraph structure, which allows the LMM to iteratively query smaller sections of a large environment. Using the datagraph in conjunction with graph traversal algorithms, we can prioritize the most relevant locations to the query, thereby improving the scalability of 3D scene language tasks. We illustrate the datagraph using 3D scenes, but these can be easily substituted by other dense modalities that represent the environment, such as pointclouds or Gaussian splats. We demonstrate the potential to use the datagraph for two 3D scene language task use cases, in a search-and-rescue mission example.

Current 3D reconstruction techniques struggle to infer unbounded scenes from a few images faithfully. Specifically, existing methods have high computational demands, require detailed pose information, and cannot reconstruct occluded regions reliably. We introduce 6Img-to-3D, an efficient, scalable transformer-based encoder-renderer method for single-shot image to 3D reconstruction. Our method outputs a 3D-consistent parameterized triplane from only six outward-facing input images for large-scale, unbounded outdoor driving scenarios. We take a step towards resolving existing shortcomings by combining contracted custom cross- and self-attention mechanisms for triplane parameterization, differentiable volume rendering, scene contraction, and image feature projection. We showcase that six surround-view vehicle images from a single timestamp without global pose information are enough to reconstruct 360$^{\circ}$ scenes during inference time, taking 395 ms. Our method allows, for example, rendering third-person images and birds-eye views. Our code is available at https://github.com/continental/6Img-to-3D, and more examples can be found at our website here https://6Img-to-3D.GitHub.io/.

Israeli security forces restricted young Palestinians from entering the Al-Aqsa Mosque in Jerusalem for prayers on Friday and deployed in strength across the Old City and beyond to quell any unrest spilling over from the conflict in Gaza. In the occupied West Bank, Israeli troops killed four Palestinians during raids, the official Palestinian news agency WAFA said. Two of the dead were identified by fighter groups as their members. Large numbers of Israeli police kept guard around Al-Aqsa, a flashpoint and often the scene of clashes, as Palestinians gathered for Friday prayers, reports said. At one point, the police fired tear gas at the Palestinians, according to Reuters.

Consider a robot tasked with tidying a desk with a meticulously constructed Lego sports car. A human may recognize that it is not socially appropriate to disassemble the sports car and put it away as part of the "tidying". How can a robot reach that conclusion? Although large language models (LLMs) have recently been used to enable social reasoning, grounding this reasoning in the real world has been challenging. To reason in the real world, robots must go beyond passively querying LLMs and *actively gather information from the environment* that is required to make the right decision. For instance, after detecting that there is an occluded car, the robot may need to actively perceive the car to know whether it is an advanced model car made out of Legos or a toy car built by a toddler. We propose an approach that leverages an LLM and vision language model (VLM) to help a robot actively perceive its environment to perform grounded social reasoning. To evaluate our framework at scale, we release the MessySurfaces dataset which contains images of 70 real-world surfaces that need to be cleaned. We additionally illustrate our approach with a robot on 2 carefully designed surfaces. We find an average 12.9% improvement on the MessySurfaces benchmark and an average 15% improvement on the robot experiments over baselines that do not use active perception. The dataset, code, and videos of our approach can be found at https://minaek.github.io/groundedsocialreasoning.