Abstract

This study investigates applying the artificial intelligence tools in translating culture-specific items in literary works. A heated debate has been initiated among scholars since the beginning of using AI tools in translation; the core of this debate was about the ability of AI translation tools to convey cultural nuances compared to human translators. Furthermore, techniques are usually adopted by AI translators in case of a lack of equivalence. To investigate these queries, fourteen excerpts containing cultural elements have been chosen from three Libyan novels by Ibrahim Al-Koni, The Bleeding of the Stones, Dust Gold and Anubis, are utilized as a case study to compare the human translator's work with AI translation tools, namely, ChatGPT and DeepL. The major results of this study are the performance of AI tools almost the same as human translators. Although AI translation tools outperformed human translators' work in some situations, they still require specialized training on culture-specific items related to specific communities. A collaboration between human and AI tools results in significant translation rather than separating or substituting each other. The mixing between machine and human leads to important progression in the field of translation.

This study reports on a collection of spiders from Misallatah Nature Reserve and National Park (MNRNP), north western Libya, including five species recorded for the first time in the country. 75 spider samples (13 adult males, 20 adult females, 42 juveniles), comprising 12 families (Agelenidae; Araneidae; Dysderidae; Gnaphosidae; Lycosidae; Palpimanidae; Pisauridae; Philodromidae; Pholcidae; Salticidae; Thomisidae; Zodariidae), 13 genera (Lycosoides Lucas, 1846; Argiope Audouin, 1826; Dysdera Latreille, 1804; Nomisia Dalmas, 1921; Hogna Simon, 1885; Palpimanus Dufour, 1820; Holocnemus Simon, 1873; Aelurillus Simon, 1885; Menemerus Simon, 1868; Plexippus C. L. Koch, 1846; Phlegra Simon, 1876; Bassaniodes Pocock, 1903; Zodarion Walckenaer, 1826) and 13 species (Argiope lobata Pallas, 1772; Dysdera crocata C. L. Koch, 1838; Nomisia recepta Pavesi, 1880; Palpimanus gibbulus Dufour, 1820; Holocnemus pluchei Scopoli, 1763; Plexippus paykulli Audouin, 1826; Bassaniodes lalandei Audouin, 1826; Zodarion pileolonotatum Denis, 1935; Lycosoides flavomaculata Lucas, 1846; Hogna effera O. Pickard-Cambridge, 1872; Aelurillus blandus Simon, 1871; Menemerus taeniatus L. Koch, 1867; Phlegra yaelae Prószyński, 1998) were collected; the last five species are first records in Libya.

The volume of Arabic text online keeps growing, and with it the need for systems that sort it into meaningful categories. A quieter problem often passes unnoticed: when long documents are split into shorter segments and pieces of the same document fall on both sides of the boundary between training and testing, the reported scores can look far better than the model deserves. This study treats that document level leakage as its central subject. Using a purpose built multidomain Arabic corpus of 1,000 segments drawn from 183 source documents across seven categories, we first measure how much leakage distorts the numbers. Under a naive random split, an optimized Linear SVM appears to reach 0.9800 accuracy; under a grouped split keyed to the document identifier, the same model reaches 0.8528. The gap of about 12.7 accuracy points is attributable to leakage, and across models the inflation ranges from roughly 11.9 to 30.4 points. Leakage also flattens the ranking, so a weak Naive Bayes looks almost as strong as the best model. Under the honest protocol, an ablation shows that character level TF IDF alone matches the combined word and character representation and slightly exceeds it on Macro F1, 0.7801 against 0.7713, while word features mainly refine precision. McNemar tests and a document clustered bootstrap show that the strongest linear models are statistically indistinguishable. A fastText baseline reaches 0.6497 accuracy, and two pretrained Arabic transformers, AraBERT and MARBERT, perform comparably to the linear model rather than surpassing it. A class level analysis exposes the General category as a structural confound whose removal raises accuracy to 0.9349. Overall, leakage aware evaluation paired with subword rich TF IDF and a Linear SVM forms a strong, honest, and practical baseline for Arabic text classification.

This study reports on a collection of spiders from Misallatah Nature Reserve and National Park (MNRNP), north western Libya, including five species recorded for the first time in the country. 75 spider samples (13 adult males, 20 adult females, 42 juveniles), comprising 12 families (Agelenidae; Araneidae; Dysderidae; Gnaphosidae; Lycosidae; Palpimanidae; Pisauridae; Philodromidae; Pholcidae; Salticidae; Thomisidae; Zodariidae), 13 genera (Lycosoides Lucas, 1846; Argiope Audouin, 1826; Dysdera Latreille, 1804; Nomisia Dalmas, 1921; Hogna Simon, 1885; Palpimanus Dufour, 1820; Holocnemus Simon, 1873; Aelurillus Simon, 1885; Menemerus Simon, 1868; Plexippus C. L. Koch, 1846; Phlegra Simon, 1876; Bassaniodes Pocock, 1903; Zodarion Walckenaer, 1826) and 13 species (Argiope lobata Pallas, 1772; Dysdera crocata C. L. Koch, 1838; Nomisia recepta Pavesi, 1880; Palpimanus gibbulus Dufour, 1820; Holocnemus pluchei Scopoli, 1763; Plexippus paykulli Audouin, 1826; Bassaniodes lalandei Audouin, 1826; Zodarion pileolonotatum Denis, 1935; Lycosoides flavomaculata Lucas, 1846; Hogna effera O. Pickard-Cambridge, 1872; Aelurillus blandus Simon, 1871; Menemerus taeniatus L. Koch, 1867; Phlegra yaelae Prószyński, 1998) were collected; the last five species are first records in Libya.

Task scheduling has often been treated as a secondary concern in fog computing, something to address only after the architecture is defined. This review argues that it is instead the central runtime decision in the fog to cloud continuum, because it determines whether the promised gains in latency, energy efficiency, and reliability can actually be achieved. The study synthesizes 102 foundational, methodological, and technical sources on task scheduling in fog enabled IoT environments. The aim was not simply to catalogue algorithms, but to examine how the field has framed the scheduling problem and how that framing has changed over time. The evidence reveals a clear progression. Early studies commonly assumed stable resources, predictable workloads, and simplified network conditions, which made scheduling easier to model but less representative of real deployments. More recent work has relaxed these assumptions and introduced dynamic, multi objective, application aware, learning based, and deployment oriented approaches. Six research streams emerge from this evolution. The main finding is that algorithmic sophistication has advanced faster than evaluation practice. Reported improvements in latency, energy consumption, and other QoS metrics are often difficult to compare because studies use different workloads, simulators, baselines, and experimental assumptions. Scheduling and orchestration overhead is rarely measured, while physical testbed validation remains limited. These gaps directly affect confidence in whether a proposed scheduler would behave as expected in operational fog systems. The review therefore identifies several priorities for future work: standardized benchmark workloads, cloud native scheduling that accounts for container lifecycle and microservice dependencies, resilience aware scheduling that treats failures and migration as first class concerns, and carbon aware orchestration that extends beyond energy minimization. Beyond the taxonomy, the paper argues for a shift from proof of concept scheduling studies toward reproducible, transparent, and deployable fog systems.